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text-to-image | null |
## Instant_cumshot
<img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - Load up your generations with this cum fiesta of a LoRA! It seems to work well on whatever checkpoint and subject you're using, including characters from other LoRAs and embeddings, realistic or anime, etc. Checkpoints that already understand nsfw are more flexible but not required.
To create the examples, I mostly started with well-known prompt/model combos from top checkpoints and then added some cumshot keywords at the end to show how the LoRA generalizes to lots of styles.
Save it as a style so you can add some pizazz to whatever you're generating at a moment's notice:
(taking a cumshot on her face, cum string, ejaculation, cum on face, cum on tits, cumshot:1.2) <lora:cumshot_49:0.4>
[](https://imagepipeline.io/models/Instant_cumshot?id=9c0b92d6-2624-484a-9ef8-8c989ff6f37e/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "sd1.5",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "9c0b92d6-2624-484a-9ef8-8c989ff6f37e",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
| {"license": "creativeml-openrail-m", "tags": ["imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic"], "pinned": false, "pipeline_tag": "text-to-image"} | imagepipeline/Instant_cumshot | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-21T11:28:40+00:00 | [] | [] | TAGS
#imagepipeline #imagepipeline.io #text-to-image #ultra-realistic #license-creativeml-openrail-m #region-us
| Instant\_cumshot
----------------
<img src="URL alt="Generated on Image Pipeline" style="border-radius: 10px;">
This lora model is uploaded on URL
Model details - Load up your generations with this cum fiesta of a LoRA! It seems to work well on whatever checkpoint and subject you're using, including characters from other LoRAs and embeddings, realistic or anime, etc. Checkpoints that already understand nsfw are more flexible but not required.
To create the examples, I mostly started with well-known prompt/model combos from top checkpoints and then added some cumshot keywords at the end to show how the LoRA generalizes to lots of styles.
Save it as a style so you can add some pizazz to whatever you're generating at a moment's notice:
```
(taking a cumshot on her face, cum string, ejaculation, cum on face, cum on tits, cumshot:1.2) <lora:cumshot_49:0.4>
```
![Try this model](URL
How to try this model ?
-----------------------
You can try using it locally or send an API call to test the output quality.
Get your 'API\_KEY' from URL. No payment required.
Coding in 'php' 'javascript' 'node' etc ? Checkout our documentation
![documentation](URL
Get more ready to use 'MODELS' like this for 'SD 1.5' and 'SDXL' :
![All models](URL
### API Reference
#### Generate Image
---
license: creativeml-openrail-m
tags:
* imagepipeline
* URL
* text-to-image
* ultra-realistic
pinned: false
pipeline\_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@URL
#### Visit Website
![portfolio](URL
If you are the original author of this model, please click here to add credits
| [
"### API Reference",
"#### Generate Image\n\n\n\n\n\n\n---\n\n\nlicense: creativeml-openrail-m\ntags:\n\n\n* imagepipeline\n* URL\n* text-to-image\n* ultra-realistic\npinned: false\npipeline\\_tag: text-to-image\n\n\n\n\n---",
"### Feedback\n\n\nIf you have any feedback, please reach out to us at hello@URL",
"#### Visit Website\n\n\n![portfolio](URL\n\n\nIf you are the original author of this model, please click here to add credits"
] | [
"TAGS\n#imagepipeline #imagepipeline.io #text-to-image #ultra-realistic #license-creativeml-openrail-m #region-us \n",
"### API Reference",
"#### Generate Image\n\n\n\n\n\n\n---\n\n\nlicense: creativeml-openrail-m\ntags:\n\n\n* imagepipeline\n* URL\n* text-to-image\n* ultra-realistic\npinned: false\npipeline\\_tag: text-to-image\n\n\n\n\n---",
"### Feedback\n\n\nIf you have any feedback, please reach out to us at hello@URL",
"#### Visit Website\n\n\n![portfolio](URL\n\n\nIf you are the original author of this model, please click here to add credits"
] |
text-generation | transformers |
# OrpoLlama-3-8B
This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 1k samples of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) created for [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3).
It's a successful fine-tune that follows the ChatML template!
**Try the demo**: https://huggingface.co/spaces/mlabonne/OrpoLlama-3-8B
## 🔎 Application
This model uses a context window of 8k. It was trained with the ChatML template.
## 🏆 Evaluation
### Nous
OrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.
Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), see the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | --------: | --------: | ---------: | --------: |
| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
| [**mlabonne/OrpoLlama-3-8B**](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) | **48.63** | **34.17** | **70.59** | **52.39** | **37.36** |
| [mlabonne/OrpoLlama-3-8B-1k](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/f41dad371d1781d0434a4672fd6f0b82) | 46.76 | 31.56 | 70.19 | 48.11 | 37.17 |
| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
`mlabonne/OrpoLlama-3-8B-1k` corresponds to a version of this model trained on 1K samples (you can see the parameters in [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3)).
### Open LLM Leaderboard
TBD.
## 📈 Training curves
You can find the experiment on W&B at [this address](https://wandb.ai/mlabonne/DPO/runs/vxnmq24z/workspace?nw=nwusermlabonne).
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/OrpoLlama-3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["orpo", "llama 3", "rlhf", "sft"], "datasets": ["mlabonne/orpo-dpo-mix-40k"]} | LoneStriker/OrpoLlama-3-8B-4.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"orpo",
"llama 3",
"rlhf",
"sft",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T11:29:01+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| OrpoLlama-3-8B
==============
.
### Open LLM Leaderboard
TBD.
Training curves
---------------
You can find the experiment on W&B at this address.
!image/png
Usage
-----
| [
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] |
video-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# timesformer-base-finetuned-k400-finetuned-kinetic400-epoch6-num_frame_10_TimeSformer_less_data
This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2313
- Accuracy: 0.92
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1616 | 0.17 | 50 | 0.4487 | 0.8 |
| 0.0055 | 1.17 | 100 | 0.0881 | 1.0 |
| 0.0029 | 2.17 | 150 | 0.0584 | 1.0 |
| 0.0005 | 3.17 | 200 | 0.2301 | 0.92 |
| 0.0004 | 4.17 | 250 | 0.2300 | 0.92 |
| 0.0004 | 5.17 | 300 | 0.1474 | 0.92 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/timesformer-base-finetuned-k400", "model-index": [{"name": "timesformer-base-finetuned-k400-finetuned-kinetic400-epoch6-num_frame_10_TimeSformer_less_data", "results": []}]} | JackWong0911/timesformer-base-finetuned-k400-finetuned-kinetic400-epoch6-num_frame_10_TimeSformer_less_data | null | [
"transformers",
"tensorboard",
"safetensors",
"timesformer",
"video-classification",
"generated_from_trainer",
"base_model:facebook/timesformer-base-finetuned-k400",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:30:54+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #timesformer #video-classification #generated_from_trainer #base_model-facebook/timesformer-base-finetuned-k400 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| timesformer-base-finetuned-k400-finetuned-kinetic400-epoch6-num\_frame\_10\_TimeSformer\_less\_data
===================================================================================================
This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2313
* Accuracy: 0.92
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 300
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.1.0+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300",
"### Training results",
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_class_no_pre_1_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_no_pre_1_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/imdb_sentiment_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_class_no_pre_1_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_sentiment_dataset",
"region:us"
] | null | 2024-04-21T11:30:55+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_1_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_1_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us \n",
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_1_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# OrpoLlama-3-8B
This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 1k samples of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) created for [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3).
It's a successful fine-tune that follows the ChatML template!
**Try the demo**: https://huggingface.co/spaces/mlabonne/OrpoLlama-3-8B
## 🔎 Application
This model uses a context window of 8k. It was trained with the ChatML template.
## 🏆 Evaluation
### Nous
OrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.
Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), see the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | --------: | --------: | ---------: | --------: |
| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
| [**mlabonne/OrpoLlama-3-8B**](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) | **48.63** | **34.17** | **70.59** | **52.39** | **37.36** |
| [mlabonne/OrpoLlama-3-8B-1k](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/f41dad371d1781d0434a4672fd6f0b82) | 46.76 | 31.56 | 70.19 | 48.11 | 37.17 |
| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
`mlabonne/OrpoLlama-3-8B-1k` corresponds to a version of this model trained on 1K samples (you can see the parameters in [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3)).
### Open LLM Leaderboard
TBD.
## 📈 Training curves
You can find the experiment on W&B at [this address](https://wandb.ai/mlabonne/DPO/runs/vxnmq24z/workspace?nw=nwusermlabonne).
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/OrpoLlama-3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["orpo", "llama 3", "rlhf", "sft"], "datasets": ["mlabonne/orpo-dpo-mix-40k"]} | LoneStriker/OrpoLlama-3-8B-5.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"orpo",
"llama 3",
"rlhf",
"sft",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"5-bit",
"region:us"
] | null | 2024-04-21T11:31:10+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us
| OrpoLlama-3-8B
==============
.
### Open LLM Leaderboard
TBD.
Training curves
---------------
You can find the experiment on W&B at this address.
!image/png
Usage
-----
| [
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us \n",
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded", "model-index": [{"name": "falcon7binstruct_mentalhealthmodel_oct23", "results": []}]} | omar-sala7/falcon7binstruct_mentalhealthmodel_oct23 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T11:32:12+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us
|
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 | [
"# falcon7binstruct_mentalhealthmodel_oct23\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 180\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.40.0\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.19.1"
] | [
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"# falcon7binstruct_mentalhealthmodel_oct23\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 180\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.40.0\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.19.1"
] |
object-detection | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2692 | 1.0 | 2500 | 1.2485 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | asldlaskdlk/detr | null | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:32:18+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
| detr
====
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2485
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2Vec2_Fine_tuned_on_RAVDESS_2_Speech_Emotion_Recognition
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english).
The dataset used to fine-tune the original pre-trained model is the [RAVDESS dataset](https://paperswithcode.com/dataset/ravdess).
This dataset provides 7442 samples of recordings from actors performing on 6 different emotions in English, which are:
```python
emotions = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'neutral', 'sad', 'surprised']
```
It achieves the following results on the evaluation set:
- Loss: 0.5638
- Accuracy: 0.8125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.1085 | 0.0694 | 10 | 2.0715 | 0.1701 |
| 2.043 | 0.1389 | 20 | 2.0531 | 0.1944 |
| 2.0038 | 0.2083 | 30 | 1.9162 | 0.3056 |
| 1.9217 | 0.2778 | 40 | 1.8085 | 0.3264 |
| 1.7814 | 0.3472 | 50 | 1.6440 | 0.3611 |
| 1.5997 | 0.4167 | 60 | 1.5428 | 0.3681 |
| 1.5293 | 0.4861 | 70 | 1.4812 | 0.4062 |
| 1.5473 | 0.5556 | 80 | 1.3423 | 0.4826 |
| 1.5098 | 0.625 | 90 | 1.3632 | 0.4653 |
| 1.1967 | 0.6944 | 100 | 1.3762 | 0.4618 |
| 1.2255 | 0.7639 | 110 | 1.3456 | 0.4618 |
| 1.6152 | 0.8333 | 120 | 1.3206 | 0.4826 |
| 1.1365 | 0.9028 | 130 | 1.3343 | 0.4792 |
| 1.1254 | 0.9722 | 140 | 1.2481 | 0.4792 |
| 1.3486 | 1.0417 | 150 | 1.4024 | 0.4688 |
| 1.2029 | 1.1111 | 160 | 1.1053 | 0.5556 |
| 1.0734 | 1.1806 | 170 | 1.1238 | 0.6181 |
| 1.029 | 1.25 | 180 | 1.3111 | 0.5347 |
| 1.0955 | 1.3194 | 190 | 1.0256 | 0.6146 |
| 0.8893 | 1.3889 | 200 | 0.9970 | 0.6389 |
| 0.8874 | 1.4583 | 210 | 0.9895 | 0.6389 |
| 0.9227 | 1.5278 | 220 | 0.8335 | 0.6667 |
| 0.7566 | 1.5972 | 230 | 0.8839 | 0.6944 |
| 0.8062 | 1.6667 | 240 | 0.8070 | 0.7118 |
| 0.6773 | 1.7361 | 250 | 0.7592 | 0.7222 |
| 0.7874 | 1.8056 | 260 | 1.1098 | 0.6285 |
| 0.8262 | 1.875 | 270 | 0.6952 | 0.7569 |
| 0.568 | 1.9444 | 280 | 0.7635 | 0.7326 |
| 0.6914 | 2.0139 | 290 | 0.6607 | 0.7917 |
| 0.6838 | 2.0833 | 300 | 0.8466 | 0.7049 |
| 0.6318 | 2.1528 | 310 | 0.6612 | 0.8056 |
| 0.604 | 2.2222 | 320 | 0.9257 | 0.6667 |
| 0.5321 | 2.2917 | 330 | 0.6067 | 0.7986 |
| 0.3421 | 2.3611 | 340 | 0.6594 | 0.7535 |
| 0.3536 | 2.4306 | 350 | 0.6525 | 0.7812 |
| 0.3087 | 2.5 | 360 | 0.6412 | 0.7812 |
| 0.4236 | 2.5694 | 370 | 0.6560 | 0.7812 |
| 0.5134 | 2.6389 | 380 | 0.6614 | 0.7882 |
| 0.5709 | 2.7083 | 390 | 0.5989 | 0.8021 |
| 0.2912 | 2.7778 | 400 | 0.6142 | 0.7951 |
| 0.516 | 2.8472 | 410 | 0.5926 | 0.7986 |
| 0.3835 | 2.9167 | 420 | 0.5797 | 0.8125 |
| 0.4055 | 2.9861 | 430 | 0.5638 | 0.8125 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-english", "model-index": [{"name": "Wav2Vec2_Fine_tuned_on_RAVDESS_2_Speech_Emotion_Recognition", "results": []}]} | Yassmen/Wav2Vec2_Fine_tuned_on_RAVDESS_2_Speech_Emotion_Recognition | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:33:00+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-english #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2\_Fine\_tuned\_on\_RAVDESS\_2\_Speech\_Emotion\_Recognition
===================================================================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english.
The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset.
This dataset provides 7442 samples of recordings from actors performing on 6 different emotions in English, which are:
It achieves the following results on the evaluation set:
* Loss: 0.5638
* Accuracy: 0.8125
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.41.0.dev0
* Pytorch 2.2.1+cu121
* Datasets 2.19.1.dev0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.1.dev0\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
# OrpoLlama-3-8B
This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 1k samples of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) created for [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3).
It's a successful fine-tune that follows the ChatML template!
**Try the demo**: https://huggingface.co/spaces/mlabonne/OrpoLlama-3-8B
## 🔎 Application
This model uses a context window of 8k. It was trained with the ChatML template.
## 🏆 Evaluation
### Nous
OrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.
Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), see the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | --------: | --------: | ---------: | --------: |
| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
| [**mlabonne/OrpoLlama-3-8B**](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) | **48.63** | **34.17** | **70.59** | **52.39** | **37.36** |
| [mlabonne/OrpoLlama-3-8B-1k](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/f41dad371d1781d0434a4672fd6f0b82) | 46.76 | 31.56 | 70.19 | 48.11 | 37.17 |
| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
`mlabonne/OrpoLlama-3-8B-1k` corresponds to a version of this model trained on 1K samples (you can see the parameters in [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3)).
### Open LLM Leaderboard
TBD.
## 📈 Training curves
You can find the experiment on W&B at [this address](https://wandb.ai/mlabonne/DPO/runs/vxnmq24z/workspace?nw=nwusermlabonne).
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/OrpoLlama-3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["orpo", "llama 3", "rlhf", "sft"], "datasets": ["mlabonne/orpo-dpo-mix-40k"]} | LoneStriker/OrpoLlama-3-8B-6.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"orpo",
"llama 3",
"rlhf",
"sft",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"6-bit",
"region:us"
] | null | 2024-04-21T11:38:32+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us
| OrpoLlama-3-8B
==============
.
### Open LLM Leaderboard
TBD.
Training curves
---------------
You can find the experiment on W&B at this address.
!image/png
Usage
-----
| [
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us \n",
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] |
text-generation | transformers | # Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Instruction format
This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
As reference, here is the pseudo-code used to tokenize instructions during fine-tuning:
```python
def tokenize(text):
return tok.encode(text, add_special_tokens=False)
[BOS_ID] +
tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_1) + [EOS_ID] +
…
tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_N) + [EOS_ID]
```
In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space.
In the Transformers library, one can use [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating) which make sure the right format is applied.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
text = "Hello my name is"
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Limitations
The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | {"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "inference": {"parameters": {"temperature": 0.5}}, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T11:39:11+00:00 | [] | [
"fr",
"it",
"de",
"es",
"en"
] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #fr #it #de #es #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our release blog post.
## Warning
This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Instruction format
This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
The template used to build a prompt for the Instruct model is defined as follows:
Note that '<s>' and '</s>' are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
As reference, here is the pseudo-code used to tokenize instructions during fine-tuning:
In the pseudo-code above, note that the 'tokenize' method should not add a BOS or EOS token automatically, but should add a prefix space.
In the Transformers library, one can use chat templates which make sure the right format is applied.
## Run the model
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note 'float16' precision only works on GPU devices
<details>
<summary> Click to expand </summary>
</details>
### Lower precision using (8-bit & 4-bit) using 'bitsandbytes'
<details>
<summary> Click to expand </summary>
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
</details>
## Limitations
The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | [
"# Model Card for Mixtral-8x7B\nThe Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.\n\nFor full details of this model please read our release blog post.",
"## Warning\nThis repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.",
"## Instruction format\n\nThis format must be strictly respected, otherwise the model will generate sub-optimal outputs.\n\nThe template used to build a prompt for the Instruct model is defined as follows:\n\nNote that '<s>' and '</s>' are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.\n\nAs reference, here is the pseudo-code used to tokenize instructions during fine-tuning:\n\n\nIn the pseudo-code above, note that the 'tokenize' method should not add a BOS or EOS token automatically, but should add a prefix space. \n\nIn the Transformers library, one can use chat templates which make sure the right format is applied.",
"## Run the model\n\n\n\nBy default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:",
"### In half-precision\n\nNote 'float16' precision only works on GPU devices\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Lower precision using (8-bit & 4-bit) using 'bitsandbytes'\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Load the model with Flash Attention 2\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"## Limitations\n\nThe Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.",
"# The Mistral AI Team\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #fr #it #de #es #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Mixtral-8x7B\nThe Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.\n\nFor full details of this model please read our release blog post.",
"## Warning\nThis repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.",
"## Instruction format\n\nThis format must be strictly respected, otherwise the model will generate sub-optimal outputs.\n\nThe template used to build a prompt for the Instruct model is defined as follows:\n\nNote that '<s>' and '</s>' are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.\n\nAs reference, here is the pseudo-code used to tokenize instructions during fine-tuning:\n\n\nIn the pseudo-code above, note that the 'tokenize' method should not add a BOS or EOS token automatically, but should add a prefix space. \n\nIn the Transformers library, one can use chat templates which make sure the right format is applied.",
"## Run the model\n\n\n\nBy default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:",
"### In half-precision\n\nNote 'float16' precision only works on GPU devices\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Lower precision using (8-bit & 4-bit) using 'bitsandbytes'\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Load the model with Flash Attention 2\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"## Limitations\n\nThe Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.",
"# The Mistral AI Team\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Mayunezu/kor_mamba_130M | null | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:41:24+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mamba #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mamba #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# OrpoLlama-3-8B
This is an ORPO fine-tune of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on 1k samples of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) created for [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3).
It's a successful fine-tune that follows the ChatML template!
**Try the demo**: https://huggingface.co/spaces/mlabonne/OrpoLlama-3-8B
## 🔎 Application
This model uses a context window of 8k. It was trained with the ChatML template.
## 🏆 Evaluation
### Nous
OrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.
Evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), see the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------: | --------: | --------: | ---------: | --------: |
| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
| [**mlabonne/OrpoLlama-3-8B**](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) | **48.63** | **34.17** | **70.59** | **52.39** | **37.36** |
| [mlabonne/OrpoLlama-3-8B-1k](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/f41dad371d1781d0434a4672fd6f0b82) | 46.76 | 31.56 | 70.19 | 48.11 | 37.17 |
| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
`mlabonne/OrpoLlama-3-8B-1k` corresponds to a version of this model trained on 1K samples (you can see the parameters in [this article](https://huggingface.co/blog/mlabonne/orpo-llama-3)).
### Open LLM Leaderboard
TBD.
## 📈 Training curves
You can find the experiment on W&B at [this address](https://wandb.ai/mlabonne/DPO/runs/vxnmq24z/workspace?nw=nwusermlabonne).
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/OrpoLlama-3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["orpo", "llama 3", "rlhf", "sft"], "datasets": ["mlabonne/orpo-dpo-mix-40k"]} | LoneStriker/OrpoLlama-3-8B-8.0bpw-h8-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"orpo",
"llama 3",
"rlhf",
"sft",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-21T11:41:27+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
| OrpoLlama-3-8B
==============
.
### Open LLM Leaderboard
TBD.
Training curves
---------------
You can find the experiment on W&B at this address.
!image/png
Usage
-----
| [
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #orpo #llama 3 #rlhf #sft #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n",
"### Nous\n\n\nOrpoLlama-4-8B outperforms Llama-3-8B-Instruct on the GPT4All and TruthfulQA datasets.\n\n\nEvaluation performed using LLM AutoEval, see the entire leaderboard here.\n\n\n\n'mlabonne/OrpoLlama-3-8B-1k' corresponds to a version of this model trained on 1K samples (you can see the parameters in this article).",
"### Open LLM Leaderboard\n\n\nTBD.\n\n\nTraining curves\n---------------\n\n\nYou can find the experiment on W&B at this address.\n\n\n!image/png\n\n\nUsage\n-----"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/openlynn/lynn-A2.7B-rp-v1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q2_K.gguf) | Q2_K | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.IQ3_XS.gguf) | IQ3_XS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.IQ3_S.gguf) | IQ3_S | 6.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q3_K_S.gguf) | Q3_K_S | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.IQ3_M.gguf) | IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q3_K_L.gguf) | Q3_K_L | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.IQ4_XS.gguf) | IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q4_K_S.gguf) | Q4_K_S | 8.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q4_K_M.gguf) | Q4_K_M | 9.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q5_K_S.gguf) | Q5_K_S | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q5_K_M.gguf) | Q5_K_M | 10.9 | |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q6_K.gguf) | Q6_K | 12.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.Q8_0.gguf) | Q8_0 | 15.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/lynn-A2.7B-rp-v1-GGUF/resolve/main/lynn-A2.7B-rp-v1.f16.gguf) | f16 | 28.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["not-for-all-audiences"], "base_model": "openlynn/lynn-A2.7B-rp-v1", "quantized_by": "mradermacher"} | mradermacher/lynn-A2.7B-rp-v1-GGUF | null | [
"transformers",
"gguf",
"not-for-all-audiences",
"en",
"base_model:openlynn/lynn-A2.7B-rp-v1",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:43:08+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #not-for-all-audiences #en #base_model-openlynn/lynn-A2.7B-rp-v1 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #not-for-all-audiences #en #base_model-openlynn/lynn-A2.7B-rp-v1 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** Alsebay
- **License:** apache-2.0
- **Finetuned from model :** Sao10K/Fimbulvetr-11B-v2
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "Sao10K/Fimbulvetr-11B-v2"} | Alsebay/test-model | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:Sao10K/Fimbulvetr-11B-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:44:00+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-Sao10K/Fimbulvetr-11B-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Alsebay
- License: apache-2.0
- Finetuned from model : Sao10K/Fimbulvetr-11B-v2
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: Alsebay\n- License: apache-2.0\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-Sao10K/Fimbulvetr-11B-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Alsebay\n- License: apache-2.0\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DNA-55/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | DNA-55/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-21T11:44:55+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] |
video-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-isl-numbers_2
This model is a fine-tuned version of [latif98/videomae-base-finetuned-isl-numbers](https://huggingface.co/latif98/videomae-base-finetuned-isl-numbers) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2759
- Accuracy: 0.6839
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3800
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5502 | 0.02 | 76 | 3.4251 | 0.0945 |
| 3.1152 | 1.02 | 152 | 3.0364 | 0.2756 |
| 2.6365 | 2.02 | 228 | 2.6197 | 0.3780 |
| 2.3879 | 3.02 | 304 | 2.1519 | 0.4646 |
| 1.9396 | 4.02 | 380 | 2.0804 | 0.4173 |
| 1.9285 | 5.02 | 456 | 1.9335 | 0.4488 |
| 1.5843 | 6.02 | 532 | 1.7907 | 0.4803 |
| 1.2387 | 7.02 | 608 | 1.8962 | 0.3858 |
| 1.2578 | 8.02 | 684 | 1.7191 | 0.4488 |
| 0.9611 | 9.02 | 760 | 1.7362 | 0.4882 |
| 0.9247 | 10.02 | 836 | 1.3898 | 0.5906 |
| 0.8107 | 11.02 | 912 | 1.9588 | 0.4094 |
| 0.7618 | 12.02 | 988 | 1.1416 | 0.6614 |
| 0.7083 | 13.02 | 1064 | 1.2812 | 0.6614 |
| 0.7098 | 14.02 | 1140 | 1.4601 | 0.5197 |
| 0.4601 | 15.02 | 1216 | 1.1276 | 0.6693 |
| 0.5684 | 16.02 | 1292 | 1.4792 | 0.5591 |
| 0.5044 | 17.02 | 1368 | 1.1236 | 0.6614 |
| 0.4551 | 18.02 | 1444 | 1.3894 | 0.6063 |
| 0.3488 | 19.02 | 1520 | 1.2918 | 0.6614 |
| 0.4711 | 20.02 | 1596 | 1.2510 | 0.6299 |
| 0.3451 | 21.02 | 1672 | 1.1265 | 0.6693 |
| 0.394 | 22.02 | 1748 | 1.1676 | 0.6378 |
| 0.234 | 23.02 | 1824 | 1.0714 | 0.7087 |
| 0.2318 | 24.02 | 1900 | 1.2647 | 0.6378 |
| 0.4294 | 25.02 | 1976 | 1.0250 | 0.7480 |
| 0.2084 | 26.02 | 2052 | 1.1361 | 0.6850 |
| 0.1724 | 27.02 | 2128 | 0.8791 | 0.7402 |
| 0.1715 | 28.02 | 2204 | 0.7549 | 0.7559 |
| 0.2719 | 29.02 | 2280 | 0.7708 | 0.7717 |
| 0.2021 | 30.02 | 2356 | 1.1394 | 0.7165 |
| 0.0999 | 31.02 | 2432 | 0.7838 | 0.7717 |
| 0.1473 | 32.02 | 2508 | 1.3809 | 0.6457 |
| 0.0939 | 33.02 | 2584 | 0.7839 | 0.7874 |
| 0.0952 | 34.02 | 2660 | 1.0636 | 0.7008 |
| 0.2684 | 35.02 | 2736 | 0.9194 | 0.7323 |
| 0.1628 | 36.02 | 2812 | 0.7346 | 0.8031 |
| 0.0584 | 37.02 | 2888 | 1.0112 | 0.7323 |
| 0.0567 | 38.02 | 2964 | 1.0584 | 0.7323 |
| 0.1358 | 39.02 | 3040 | 1.0566 | 0.7323 |
| 0.0796 | 40.02 | 3116 | 0.9323 | 0.7480 |
| 0.0828 | 41.02 | 3192 | 0.7611 | 0.7953 |
| 0.0661 | 42.02 | 3268 | 0.7284 | 0.7874 |
| 0.0882 | 43.02 | 3344 | 0.6982 | 0.7953 |
| 0.0398 | 44.02 | 3420 | 0.8586 | 0.7717 |
| 0.2085 | 45.02 | 3496 | 0.7990 | 0.7717 |
| 0.0509 | 46.02 | 3572 | 0.7134 | 0.8268 |
| 0.0791 | 47.02 | 3648 | 0.6887 | 0.8189 |
| 0.0469 | 48.02 | 3724 | 0.7159 | 0.8031 |
| 0.0621 | 49.02 | 3800 | 0.7062 | 0.8031 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "latif98/videomae-base-finetuned-isl-numbers", "model-index": [{"name": "videomae-base-finetuned-isl-numbers_2", "results": []}]} | latif98/videomae-base-finetuned-isl-numbers_2 | null | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:latif98/videomae-base-finetuned-isl-numbers",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2024-04-21T11:45:23+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-latif98/videomae-base-finetuned-isl-numbers #license-cc-by-nc-4.0 #endpoints_compatible #region-us #has_space
| videomae-base-finetuned-isl-numbers\_2
======================================
This model is a fine-tuned version of latif98/videomae-base-finetuned-isl-numbers on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2759
* Accuracy: 0.6839
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 3800
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.1.0+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 3800",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-latif98/videomae-base-finetuned-isl-numbers #license-cc-by-nc-4.0 #endpoints_compatible #region-us #has_space \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 3800",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | DevanshArora2002/legal-PPO-model | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:45:47+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Model Sources [optional]
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## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | LoneStriker/dolphin-2.9-llama3-8b-3.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
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"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"3-bit",
"region:us"
] | null | 2024-04-21T11:46:32+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.24 +/- 0.15", "name": "mean_reward", "verified": false}]}]}]} | jiaqianwu/a2c-PandaReachDense-v3 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-21T11:47:46+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_class_no_pre_2_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_no_pre_2_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/imdb_sentiment_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_class_no_pre_2_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_sentiment_dataset",
"region:us"
] | null | 2024-04-21T11:48:16+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_2_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_2_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us \n",
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_2_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
object-detection | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1069 | 0.2 | 500 | 1.4327 |
| 1.2885 | 0.4 | 1000 | 1.1961 |
| 1.1377 | 0.6 | 1500 | 1.0482 |
| 1.0395 | 0.8 | 2000 | 0.9764 |
| 1.0037 | 1.0 | 2500 | 0.9499 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | alizeyn/detr | null | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:48:17+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
| detr
====
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9499
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | LoneStriker/dolphin-2.9-llama3-8b-4.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T11:48:27+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #4-bit #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #4-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | LoneStriker/dolphin-2.9-llama3-8b-5.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"5-bit",
"region:us"
] | null | 2024-04-21T11:50:39+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_instruct_generation
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7128 | 1.0 | 1271 | 0.6649 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mixtral-8x7B-Instruct-v0.1", "model-index": [{"name": "mistral_instruct_generation", "results": []}]} | Cem13/3mixi | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T11:53:08+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us
| mistral\_instruct\_generation
=============================
This model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6649
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_steps: 0.03
* num\_epochs: 1
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Tokenizers 0.19.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-Instruct-v0.1 #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | LoneStriker/dolphin-2.9-llama3-8b-6.0bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"6-bit",
"region:us"
] | null | 2024-04-21T11:53:10+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
reinforcement-learning | transformers |
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="baek26//tmp/tmpvdc63lls/baek26/all_9991_all_8657_bart-base_rl")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmpvdc63lls/baek26/all_9991_all_8657_bart-base_rl")
model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmpvdc63lls/baek26/all_9991_all_8657_bart-base_rl")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
| {"license": "apache-2.0", "tags": ["trl", "ppo", "transformers", "reinforcement-learning"]} | baek26/all_9991_all_8657_bart-base_rl | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:53:31+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #trl #ppo #reinforcement-learning #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TRL Model
This is a TRL language model that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
You can then generate text as follows:
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
| [
"# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.",
"## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:"
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #trl #ppo #reinforcement-learning #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.",
"## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Kquant03/BurningBruce-SOLAR-8x10.7B-bf16
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q2_K.gguf) | i1-Q2_K | 26.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.0 | |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-IQ3_S.gguf) | i1-IQ3_S | 30.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-IQ3_M.gguf) | i1-IQ3_M | 32.2 | |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-IQ4_XS.gguf) | i1-IQ4_XS | 37.6 | |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q4_0.gguf) | i1-Q4_0 | 39.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.3 | |
| [GGUF](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q5_K_M.gguf) | i1-Q5_K_M | 49.8 | |
| [PART 1](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF/resolve/main/BurningBruce-SOLAR-8x10.7B-bf16.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 57.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge", "moe"], "base_model": "Kquant03/BurningBruce-SOLAR-8x10.7B-bf16", "quantized_by": "mradermacher"} | mradermacher/BurningBruce-SOLAR-8x10.7B-bf16-i1-GGUF | null | [
"transformers",
"gguf",
"merge",
"moe",
"en",
"base_model:Kquant03/BurningBruce-SOLAR-8x10.7B-bf16",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T11:53:49+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #merge #moe #en #base_model-Kquant03/BurningBruce-SOLAR-8x10.7B-bf16 #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
weighted/imatrix quants of URL
static quants are available at URL
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #merge #moe #en #base_model-Kquant03/BurningBruce-SOLAR-8x10.7B-bf16 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | LoneStriker/dolphin-2.9-llama3-8b-8.0bpw-h8-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-21T11:56:02+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
null | transformers | # Emma - Modelo de Texto a Voz basado en ONNX Runtime
Bienvenido al repositorio del modelo de texto a voz Emma, basado en la arquitectura ONNX Runtime. Este modelo ha sido entrenado utilizando Piper, una plataforma de aprendizaje automático de última generación.
## Acerca de Emma
Emma es un modelo de texto a voz diseñado para ofrecer una conversión precisa y natural de texto a voz. Se basa en la arquitectura ONNX Runtime para un rendimiento óptimo y eficiente. El modelo ha sido entrenado usando Piper, asegurando un alto nivel de calidad en la conversión de texto a voz.
## Características
- **Arquitectura ONNX Runtime**: Proporciona un rendimiento eficiente y de alta calidad.
- **Entrenado con Piper**: Garantiza una conversión de texto a voz precisa y natural.
- **Fácil de usar**: Simple de integrar en tus proyectos de texto a voz.
## Cómo probar Emma
Puedes probar el modelo Emma accediendo a [tts.hircoir.eu.org](http://tts.hircoir.eu.org). Allí podrás experimentar la calidad de la conversión de texto a voz que ofrece Emma.
## Uso
Por el momento, el modelo Emma no ha sido liberado públicamente. Sin embargo, puedes acceder a la demostración en [tts.hircoir.eu.org](http://tts.hircoir.eu.org) para probarlo.
## Contribuciones
Si tienes alguna sugerencia o comentario, no dudes en ponerte en contacto con nosotros o abrir un issue en este repositorio.
---
¡Gracias por visitar el repositorio de Emma! Si tienes alguna pregunta o inquietud, no dudes en ponerte en contacto con nosotros. | {"tags": ["emma", "piper", "piper tts", "onnxrutime"]} | HirCoir/piper-emma-neuronal | null | [
"transformers",
"emma",
"piper",
"piper tts",
"onnxrutime",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:02:17+00:00 | [] | [] | TAGS
#transformers #emma #piper #piper tts #onnxrutime #endpoints_compatible #region-us
| # Emma - Modelo de Texto a Voz basado en ONNX Runtime
!Emma
Bienvenido al repositorio del modelo de texto a voz Emma, basado en la arquitectura ONNX Runtime. Este modelo ha sido entrenado utilizando Piper, una plataforma de aprendizaje automático de última generación.
## Acerca de Emma
Emma es un modelo de texto a voz diseñado para ofrecer una conversión precisa y natural de texto a voz. Se basa en la arquitectura ONNX Runtime para un rendimiento óptimo y eficiente. El modelo ha sido entrenado usando Piper, asegurando un alto nivel de calidad en la conversión de texto a voz.
## Características
- Arquitectura ONNX Runtime: Proporciona un rendimiento eficiente y de alta calidad.
- Entrenado con Piper: Garantiza una conversión de texto a voz precisa y natural.
- Fácil de usar: Simple de integrar en tus proyectos de texto a voz.
## Cómo probar Emma
Puedes probar el modelo Emma accediendo a URL. Allí podrás experimentar la calidad de la conversión de texto a voz que ofrece Emma.
## Uso
Por el momento, el modelo Emma no ha sido liberado públicamente. Sin embargo, puedes acceder a la demostración en URL para probarlo.
## Contribuciones
Si tienes alguna sugerencia o comentario, no dudes en ponerte en contacto con nosotros o abrir un issue en este repositorio.
---
¡Gracias por visitar el repositorio de Emma! Si tienes alguna pregunta o inquietud, no dudes en ponerte en contacto con nosotros. | [
"# Emma - Modelo de Texto a Voz basado en ONNX Runtime\n\n!Emma\n\nBienvenido al repositorio del modelo de texto a voz Emma, basado en la arquitectura ONNX Runtime. Este modelo ha sido entrenado utilizando Piper, una plataforma de aprendizaje automático de última generación.",
"## Acerca de Emma\n\nEmma es un modelo de texto a voz diseñado para ofrecer una conversión precisa y natural de texto a voz. Se basa en la arquitectura ONNX Runtime para un rendimiento óptimo y eficiente. El modelo ha sido entrenado usando Piper, asegurando un alto nivel de calidad en la conversión de texto a voz.",
"## Características\n\n- Arquitectura ONNX Runtime: Proporciona un rendimiento eficiente y de alta calidad.\n- Entrenado con Piper: Garantiza una conversión de texto a voz precisa y natural.\n- Fácil de usar: Simple de integrar en tus proyectos de texto a voz.",
"## Cómo probar Emma\n\nPuedes probar el modelo Emma accediendo a URL. Allí podrás experimentar la calidad de la conversión de texto a voz que ofrece Emma.",
"## Uso\n\nPor el momento, el modelo Emma no ha sido liberado públicamente. Sin embargo, puedes acceder a la demostración en URL para probarlo.",
"## Contribuciones\n\nSi tienes alguna sugerencia o comentario, no dudes en ponerte en contacto con nosotros o abrir un issue en este repositorio.\n\n---\n\n¡Gracias por visitar el repositorio de Emma! Si tienes alguna pregunta o inquietud, no dudes en ponerte en contacto con nosotros."
] | [
"TAGS\n#transformers #emma #piper #piper tts #onnxrutime #endpoints_compatible #region-us \n",
"# Emma - Modelo de Texto a Voz basado en ONNX Runtime\n\n!Emma\n\nBienvenido al repositorio del modelo de texto a voz Emma, basado en la arquitectura ONNX Runtime. Este modelo ha sido entrenado utilizando Piper, una plataforma de aprendizaje automático de última generación.",
"## Acerca de Emma\n\nEmma es un modelo de texto a voz diseñado para ofrecer una conversión precisa y natural de texto a voz. Se basa en la arquitectura ONNX Runtime para un rendimiento óptimo y eficiente. El modelo ha sido entrenado usando Piper, asegurando un alto nivel de calidad en la conversión de texto a voz.",
"## Características\n\n- Arquitectura ONNX Runtime: Proporciona un rendimiento eficiente y de alta calidad.\n- Entrenado con Piper: Garantiza una conversión de texto a voz precisa y natural.\n- Fácil de usar: Simple de integrar en tus proyectos de texto a voz.",
"## Cómo probar Emma\n\nPuedes probar el modelo Emma accediendo a URL. Allí podrás experimentar la calidad de la conversión de texto a voz que ofrece Emma.",
"## Uso\n\nPor el momento, el modelo Emma no ha sido liberado públicamente. Sin embargo, puedes acceder a la demostración en URL para probarlo.",
"## Contribuciones\n\nSi tienes alguna sugerencia o comentario, no dudes en ponerte en contacto con nosotros o abrir un issue en este repositorio.\n\n---\n\n¡Gracias por visitar el repositorio de Emma! Si tienes alguna pregunta o inquietud, no dudes en ponerte en contacto con nosotros."
] |
null | peft |
test parameter by use split="test"
code to create dataset
```python
import random
alpaca_prompt = """<original>{}</original>
<translate to="{}">{}"""
BOS_TOKEN = tokenizer.bos_token # Must add EOS_TOKEN
EOS_TOKEN = "</translate>"+tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
translations = examples["translation"]
texts = []
text_en = ""
text_th = ""
translate_to = 'th'
max_group_count = 1
group_count = 0
for translation in translations:
if group_count >= max_group_count:
if(translate_to == 'th'):
text = alpaca_prompt.format(text_en, translate_to, text_th) + EOS_TOKEN
else:
text = alpaca_prompt.format(text_th, translate_to, text_en) + EOS_TOKEN
texts.append(text)
text_en = ""
text_th = ""
max_group_count = random.randint(1, 5)
group_count = 0
translate_to = random.choice(['en', 'th'])
num_newlines = random.randint(1, 5)
newlines = '\n' * num_newlines
if(text_en == ""):
text_en = translation['en']
text_th = translation['th']
else:
text_en = text_en+newlines+translation['en']
text_th = text_th+newlines+translation['th']
group_count = group_count+1
if(translate_to == 'th'):
text = alpaca_prompt.format(text_en, translate_to, text_th) + EOS_TOKEN
else:
text = alpaca_prompt.format(text_th, translate_to, text_en) + EOS_TOKEN
texts.append(text)
return { "text" : texts, }
from datasets import load_dataset
dataset = load_dataset("scb_mt_enth_2020",'enth',split="test")
dataset = dataset.map(formatting_prompts_func, batched = True,remove_columns=["translation",'subdataset'])
dataset = dataset.train_test_split(test_size=0.1, shuffle=True)
dataset['train'][0:5]
``` | {"library_name": "peft", "base_model": "unsloth/gemma-7b-bnb-4bit"} | ping98k/gemma-7b-translator-0.2-lora | null | [
"peft",
"safetensors",
"base_model:unsloth/gemma-7b-bnb-4bit",
"region:us"
] | null | 2024-04-21T12:05:10+00:00 | [] | [] | TAGS
#peft #safetensors #base_model-unsloth/gemma-7b-bnb-4bit #region-us
|
test parameter by use split="test"
code to create dataset
| [] | [
"TAGS\n#peft #safetensors #base_model-unsloth/gemma-7b-bnb-4bit #region-us \n"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_class_no_pre_3_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_no_pre_3_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/imdb_sentiment_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_class_no_pre_3_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_sentiment_dataset",
"region:us"
] | null | 2024-04-21T12:05:39+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_3_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_3_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us \n",
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_3_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | null | # llama-3-neural-chat-v1-8b
- Model creator: [Locutusque](https://huggingface.co/Locutusque)
- Original model: [llama-3-neural-chat-v1-8b](https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Locutusque's llama-3-neural-chat-v1-8b ](https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b).
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-3-neural-chat-v1-8b.Q2_K.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q2_K.gguf ) | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes |
| [llama-3-neural-chat-v1-8b.Q3_K_M.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q3_K_M.gguf ) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [llama-3-neural-chat-v1-8b.Q4_K_S.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q4_K_S.gguf ) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [llama-3-neural-chat-v1-8b.Q4_K_M.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q4_K_M.gguf ) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [llama-3-neural-chat-v1-8b.Q5_K_M.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q5_K_M.gguf ) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [llama-3-neural-chat-v1-8b.Q6_K.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q6_K.gguf ) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [llama-3-neural-chat-v1-8b.Q8_0.gguf ](https://huggingface.co/seyf1elislam/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b.Q8_0.gguf ) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | | {"tags": ["GGUF"], "base_model": ["Locutusque/llama-3-neural-chat-v1-8b"]} | seyf1elislam/llama-3-neural-chat-v1-8b-GGUF | null | [
"gguf",
"GGUF",
"base_model:Locutusque/llama-3-neural-chat-v1-8b",
"region:us"
] | null | 2024-04-21T12:07:08+00:00 | [] | [] | TAGS
#gguf #GGUF #base_model-Locutusque/llama-3-neural-chat-v1-8b #region-us
| llama-3-neural-chat-v1-8b
=========================
* Model creator: Locutusque
* Original model: llama-3-neural-chat-v1-8b
Description
-----------
This repo contains GGUF format model files for Locutusque's llama-3-neural-chat-v1-8b .
Provided files
--------------
| [] | [
"TAGS\n#gguf #GGUF #base_model-Locutusque/llama-3-neural-chat-v1-8b #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-sw
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1784
- Wer: 282.7586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0997 | 14.2857 | 100 | 2.9052 | 195.6897 |
| 0.0013 | 28.5714 | 200 | 3.1173 | 310.3448 |
| 0.0008 | 42.8571 | 300 | 3.1651 | 281.8966 |
| 0.0007 | 57.1429 | 400 | 3.1784 | 282.7586 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "whisper-small-sw", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_11_0", "type": "common_voice_11_0", "config": "rw", "split": "None", "args": "rw"}, "metrics": [{"type": "wer", "value": 282.7586206896552, "name": "Wer"}]}]}]} | diane20000000000000/whisper-small-sw | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:07:32+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| whisper-small-sw
================
This model is a fine-tuned version of openai/whisper-small on the common\_voice\_11\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1784
* Wer: 282.7586
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 400
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 400\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 400\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
text-generation | transformers | This is a model that extracts biographical information on historical actors and writes it in Danish to a CSV file in the Fichoz format for the database Nordiske Folk (NoFo). It is based on Llama-3-8B-instruct, and has been trained on 400 hundred short biographies in various languages.
How to prompt:
Instruction:
You are a Danish research assistant. Please write the information provided by the user as CSV lines, using the following format:
\<Name\>;\<Event\>;\<Place\>;\<Year\>=\<Month\>=\<Day\>;\<End Year\>=\<End Month\>=\<End Day\>
Include only events with a known year. \n\nInclude end date only if stated, otherwise leave blank. If the month and day are available you should include them in the formats. Otherwise use "=00=00" to indicate their absence.
Instead of = to denote dates use < to denote before and > to denote after, such as 1525<05<12 for before May 12th 1525, or 1525>05>12 for after May 12th 1525. Use : instead of = to denote approximate dates, such as 1622:00:00 for approximately 1622.
Write only in modern Danish, translating when necessary. Use modern Danish spelling and capitalization.
Input:
[Biographical text here]
Output:
[CSV output comes here]
---
license: apache-2.0
--- | {"language": ["da"], "license": "apache-2.0", "pipeline_tag": "text-generation"} | GWK/NoFo-llama-3-8b-instruct-v1 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:08:48+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #llama #text-generation #conversational #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is a model that extracts biographical information on historical actors and writes it in Danish to a CSV file in the Fichoz format for the database Nordiske Folk (NoFo). It is based on Llama-3-8B-instruct, and has been trained on 400 hundred short biographies in various languages.
How to prompt:
Instruction:
You are a Danish research assistant. Please write the information provided by the user as CSV lines, using the following format:
\<Name\>;\<Event\>;\<Place\>;\<Year\>=\<Month\>=\<Day\>;\<End Year\>=\<End Month\>=\<End Day\>
Include only events with a known year. \n\nInclude end date only if stated, otherwise leave blank. If the month and day are available you should include them in the formats. Otherwise use "=00=00" to indicate their absence.
Instead of = to denote dates use < to denote before and > to denote after, such as 1525<05<12 for before May 12th 1525, or 1525>05>12 for after May 12th 1525. Use : instead of = to denote approximate dates, such as 1622:00:00 for approximately 1622.
Write only in modern Danish, translating when necessary. Use modern Danish spelling and capitalization.
Input:
[Biographical text here]
Output:
[CSV output comes here]
---
license: apache-2.0
--- | [] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
object-detection | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7508 | 0.16 | 300 | 1.7472 |
| 1.412 | 0.32 | 600 | 1.3611 |
| 1.2549 | 0.48 | 900 | 1.1682 |
| 1.0701 | 0.64 | 1200 | 1.0696 |
| 0.9812 | 0.8 | 1500 | 1.0103 |
| 1.0346 | 0.96 | 1800 | 0.9782 |
| 1.014 | 1.12 | 2100 | 0.9429 |
| 0.9691 | 1.28 | 2400 | 0.9248 |
| 0.9749 | 1.44 | 2700 | 0.8947 |
| 0.9707 | 1.6 | 3000 | 0.8887 |
| 0.938 | 1.76 | 3300 | 0.8889 |
| 0.9477 | 1.92 | 3600 | 0.8756 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | depek/detr | null | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:10:48+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
| detr
====
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8756
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.39.1
* Pytorch 2.2.1
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.1\n* Pytorch 2.2.1\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.1\n* Pytorch 2.2.1\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/FreedomIntelligence/Apollo-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q2_K.gguf) | Q2_K | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.IQ3_XS.gguf) | IQ3_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.IQ3_S.gguf) | IQ3_S | 4.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q3_K_S.gguf) | Q3_K_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q4_K_S.gguf) | Q4_K_S | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q5_K_S.gguf) | Q5_K_S | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q5_K_M.gguf) | Q5_K_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q6_K.gguf) | Q6_K | 7.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Apollo-7B-GGUF/resolve/main/Apollo-7B.Q8_0.gguf) | Q8_0 | 9.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "FreedomIntelligence/Apollo-7B", "quantized_by": "mradermacher"} | mradermacher/Apollo-7B-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:FreedomIntelligence/Apollo-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:10:58+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-FreedomIntelligence/Apollo-7B #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-FreedomIntelligence/Apollo-7B #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | null | # dolphin-2.9-llama3-8b
- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
- Original model: [dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [cognitivecomputations's dolphin-2.9-llama3-8b ](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b).
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [dolphin-2.9-llama3-8b.Q2_K.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q2_K.gguf ) | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes |
| [dolphin-2.9-llama3-8b.Q3_K_M.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q3_K_M.gguf ) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [dolphin-2.9-llama3-8b.Q4_K_S.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q4_K_S.gguf ) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [dolphin-2.9-llama3-8b.Q4_K_M.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q4_K_M.gguf ) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [dolphin-2.9-llama3-8b.Q5_K_M.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q5_K_M.gguf ) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [dolphin-2.9-llama3-8b.Q6_K.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q6_K.gguf ) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [dolphin-2.9-llama3-8b.Q8_0.gguf ](https://huggingface.co/seyf1elislam/dolphin-2.9-llama3-8b-GGUF/blob/main/dolphin-2.9-llama3-8b.Q8_0.gguf ) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | | {"tags": ["GGUF"], "base_model": ["cognitivecomputations/dolphin-2.9-llama3-8b"]} | seyf1elislam/dolphin-2.9-llama3-8b-GGUF | null | [
"gguf",
"GGUF",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"region:us"
] | null | 2024-04-21T12:11:43+00:00 | [] | [] | TAGS
#gguf #GGUF #base_model-cognitivecomputations/dolphin-2.9-llama3-8b #region-us
| dolphin-2.9-llama3-8b
=====================
* Model creator: cognitivecomputations
* Original model: dolphin-2.9-llama3-8b
Description
-----------
This repo contains GGUF format model files for cognitivecomputations's dolphin-2.9-llama3-8b .
Provided files
--------------
| [] | [
"TAGS\n#gguf #GGUF #base_model-cognitivecomputations/dolphin-2.9-llama3-8b #region-us \n"
] |
null | null | # lightblue-ao-karasu-72B-gguf
[lightblueさんが公開しているao-karasu-72B](https://huggingface.co/lightblue/ao-karasu-72B)のggufフォーマット変換版です。
Qwen1.5-72B-Chatベースになります。
## licence
tongyi-qianwenライセンスになります。
ご使用前に必ず[こちらのライセンス](https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE)をご確認ください。
## 他のモデル
[mmnga/lightblue-ao-karasu-72B-gguf](https://huggingface.co/mmnga/lightblue-ao-karasu-72B-gguf)
[mmnga/lightblue-karasu-1.1B-gguf](https://huggingface.co/mmnga/lightblue-karasu-1.1B-gguf)
[mmnga/lightblue-karasu-7B-chat-plus-unleashed-gguf](https://huggingface.co/mmnga/lightblue-karasu-7B-chat-plus-unleashed-gguf)
[mmnga/lightblue-qarasu-14B-chat-plus-unleashed-gguf](https://huggingface.co/mmnga/lightblue-qarasu-14B-chat-plus-unleashed-gguf)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'lightblue-ao-karasu-72B-Q4_0.gguf' -p "<|im_start|>system\nあなたはAIアシスタントです。<|im_end|>\n<|im_start|>user\n今夜の夕食のレシピを教えて<|im_end|>" -n 128
``` | {"language": ["en", "ja"], "license": "other", "tags": ["qwen-1.5"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE"} | mmnga/lightblue-ao-karasu-72B-gguf | null | [
"gguf",
"qwen-1.5",
"en",
"ja",
"license:other",
"region:us"
] | null | 2024-04-21T12:12:48+00:00 | [] | [
"en",
"ja"
] | TAGS
#gguf #qwen-1.5 #en #ja #license-other #region-us
| # lightblue-ao-karasu-72B-gguf
lightblueさんが公開しているao-karasu-72Bのggufフォーマット変換版です。
Qwen1.5-72B-Chatベースになります。
## licence
tongyi-qianwenライセンスになります。
ご使用前に必ずこちらのライセンスをご確認ください。
## 他のモデル
mmnga/lightblue-ao-karasu-72B-gguf
mmnga/lightblue-karasu-1.1B-gguf
mmnga/lightblue-karasu-7B-chat-plus-unleashed-gguf
mmnga/lightblue-qarasu-14B-chat-plus-unleashed-gguf
## Usage
| [
"# lightblue-ao-karasu-72B-gguf \nlightblueさんが公開しているao-karasu-72Bのggufフォーマット変換版です。\nQwen1.5-72B-Chatベースになります。",
"## licence \ntongyi-qianwenライセンスになります。\nご使用前に必ずこちらのライセンスをご確認ください。",
"## 他のモデル\nmmnga/lightblue-ao-karasu-72B-gguf \nmmnga/lightblue-karasu-1.1B-gguf \nmmnga/lightblue-karasu-7B-chat-plus-unleashed-gguf \nmmnga/lightblue-qarasu-14B-chat-plus-unleashed-gguf",
"## Usage"
] | [
"TAGS\n#gguf #qwen-1.5 #en #ja #license-other #region-us \n",
"# lightblue-ao-karasu-72B-gguf \nlightblueさんが公開しているao-karasu-72Bのggufフォーマット変換版です。\nQwen1.5-72B-Chatベースになります。",
"## licence \ntongyi-qianwenライセンスになります。\nご使用前に必ずこちらのライセンスをご確認ください。",
"## 他のモデル\nmmnga/lightblue-ao-karasu-72B-gguf \nmmnga/lightblue-karasu-1.1B-gguf \nmmnga/lightblue-karasu-7B-chat-plus-unleashed-gguf \nmmnga/lightblue-qarasu-14B-chat-plus-unleashed-gguf",
"## Usage"
] |
null | null |
Pretrained Model for the Land Cover Segmentation Dataset.
| {"license": "mit"} | lightning-uq-box/loveda_bnn_resnet18 | null | [
"license:mit",
"region:us"
] | null | 2024-04-21T12:13:17+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Pretrained Model for the Land Cover Segmentation Dataset.
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
null | null |
Pretrained model for land cover segmentation tuturial.
| {"license": "mit"} | lightning-uq-box/loveda_probunet_resnet18 | null | [
"license:mit",
"region:us"
] | null | 2024-04-21T12:14:40+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Pretrained model for land cover segmentation tuturial.
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-generation | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | sparsh35/finetunedstarbetaessawq | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T12:14:41+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
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Use the code below to get started with the model.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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[optional]
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"} | Charishma27/sft_mistral_709_steps_3_apple_epoch | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-04-21T12:15:09+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us
|
# Model Card for Model ID
## Model Details
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
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"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/dumbo-llama3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:19:26+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Contact"
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_class_no_pre_4_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_no_pre_4_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/imdb_sentiment_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_class_no_pre_4_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_sentiment_dataset",
"region:us"
] | null | 2024-04-21T12:20:57+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_4_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_no_pre_4_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us \n",
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned on the Qwen1.5-4B-Chat model and COIG-CQIA/ruozhiba dataset by QLoRA.
Be noted that the model file contains the adapter LoRA weights only, so you are suggested to merge adapters with the base model for inference usage. Check the official reference [here](https://huggingface.co/docs/peft/main/en/developer_guides/lora#merge-adapters)
This whole training process is running on Google Colab with free computing resources, detail can be accessed via [link](https://colab.research.google.com/drive/1GiI8drsinxhFdprWbqlXtN0DqbHHs1fe?hl=en#scrollTo=5o3OgCMdRGgp)
This project is for the demonstration only of course DSAA5009, HKUST Guangzhou | {"language": ["zh"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["m-a-p/COIG-CQIA"], "pipeline_tag": "text-generation", "inference": false} | Ti-ger/Qwen1.5-4B-CQIA | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"zh",
"dataset:m-a-p/COIG-CQIA",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T12:23:19+00:00 | [] | [
"zh"
] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #zh #dataset-m-a-p/COIG-CQIA #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
This model is fine-tuned on the Qwen1.5-4B-Chat model and COIG-CQIA/ruozhiba dataset by QLoRA.
Be noted that the model file contains the adapter LoRA weights only, so you are suggested to merge adapters with the base model for inference usage. Check the official reference here
This whole training process is running on Google Colab with free computing resources, detail can be accessed via link
This project is for the demonstration only of course DSAA5009, HKUST Guangzhou | [
"# Model Card for Model ID\n\n\n\nThis model is fine-tuned on the Qwen1.5-4B-Chat model and COIG-CQIA/ruozhiba dataset by QLoRA. \n\nBe noted that the model file contains the adapter LoRA weights only, so you are suggested to merge adapters with the base model for inference usage. Check the official reference here\n\nThis whole training process is running on Google Colab with free computing resources, detail can be accessed via link\n\nThis project is for the demonstration only of course DSAA5009, HKUST Guangzhou"
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #zh #dataset-m-a-p/COIG-CQIA #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID\n\n\n\nThis model is fine-tuned on the Qwen1.5-4B-Chat model and COIG-CQIA/ruozhiba dataset by QLoRA. \n\nBe noted that the model file contains the adapter LoRA weights only, so you are suggested to merge adapters with the base model for inference usage. Check the official reference here\n\nThis whole training process is running on Google Colab with free computing resources, detail can be accessed via link\n\nThis project is for the demonstration only of course DSAA5009, HKUST Guangzhou"
] |
token-classification | span-marker |
# SpanMarker
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.
## Model Details
### Model Description
- **Model Type:** SpanMarker
<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Maximum Entity Length:** 16 words
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### Model Labels
| Label | Examples |
|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
| person | "Barney Glaser", "Malcolm Gladwell", "Charles Duhigg" |
| publication_date | "2000", "1967", "2018" |
| publisher | "Little , Brown and Company", "Sociology Press", "Avery" |
| work_of_art | "`` The Tipping Point : How Little Things Can Make a Big Difference ''", "`` The Power of Habit ''", "`` The Discovery of Grounded Theory ''" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:-----------------|:----------|:-------|:----|
| **all** | 0.0 | 0.0 | 0.0 |
| person | 0.0 | 0.0 | 0.0 |
| publication_date | 0.0 | 0.0 | 0.0 |
| publisher | 0.0 | 0.0 | 0.0 |
| work_of_art | 0.0 | 0.0 | 0.0 |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("\"The Pragmatic Turn\" (2020, University of Pennsylvania Press) provides key insights into pragmatist philosophy, edited by John J. Stuhr . For provocative science, try \"Introducing Consciousness\", Alex Westrin and Vidyut Lokhande's 2018 work published via Icon Books, challenging dominant models of self-awareness.")
```
### Downstream Use
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
```python
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")
```
</details>
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:----------------------|:----|:---------|:----|
| Sentence length | 47 | 104.6034 | 200 |
| Entities per sentence | 3 | 4.0036 | 5 |
### Training Hyperparameters
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:-----:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 1.0 | 563 | 0.0206 | 0.0 | 0.0 | 0.0 | 0.8513 |
| 2.0 | 1126 | 0.0173 | 0.0 | 0.0 | 0.0 | 0.8513 |
| 3.0 | 1689 | 0.0162 | 0.0 | 0.0 | 0.0 | 0.8513 |
### Framework Versions
- Python: 3.10.13
- SpanMarker: 1.5.1.dev
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.16.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "span-marker", "tags": ["span-marker", "token-classification", "ner", "named-entity-recognition", "generated_from_span_marker_trainer"], "metrics": ["precision", "recall", "f1"], "widget": [{"text": "In the 2017 publication \"The Routledge Handbook of Collective Intentionality\", edited by Kirk Ludwig and Marija Jankovic, and released by Routledge, leading scholars explored the complex concept of collective intentionality and its implications for various disciplines, including philosophy, cognitive science, and social theory . A thought-provoking 2015 article titled \"The Uncivilization Thesis: A Critique\" by Megan Gittins, published in Environmental Ethics, offered a critical examination of the controversial \"uncivilization thesis\" and its implications for our understanding of the relationship between civilization and environmental sustainability."}, {"text": "In the \"The Selfish Gene\", renowned biologist Richard Dawkins introduced the revolutionary concept of the \"selfish gene\" in 1976, published by Oxford University Press . This influential work challenged traditional views of evolution and sparked widespread discussions about the nature of altruism and cooperation . Fans of science writing might appreciate \"A Short History of Nearly Everything\" by Bill Bryson, a captivating exploration of the vast realms of scientific knowledge published by Broadway Books in 2003."}, {"text": "\"The Pragmatic Turn\" (2020, University of Pennsylvania Press) provides key insights into pragmatist philosophy, edited by John J. Stuhr . For provocative science, try \"Introducing Consciousness\", Alex Westrin and Vidyut Lokhande's 2018 work published via Icon Books, challenging dominant models of self-awareness."}, {"text": "Have you read \"The Selfish Gene\" by Richard Dawkins? Published in 1976 by Oxford University Press, this seminal work introduced the gene-centric view of evolution and proposed the controversial concept of the \"extended phenotype .\" Dawkins' ideas sparked intense debates and influenced diverse fields like evolutionary biology, psychology, and memetics . Daniel C. Dennett's \"Darwin's Dangerous Idea\" (1995, Simon & Schuster) is another must-read that explores the far-reaching implications of evolutionary theory, from the origins of life to the nature of human consciousness and free will."}, {"text": "\"The Sociology of Philosophies\", an insightful book penned by Randall Collins and published in 1998 by Harvard University Press, examined the social factors that influence the development and trajectory of philosophical thought throughout history . Collins' analysis shed light on how philosophical ideas are shaped by the broader cultural, political, and intellectual contexts in which they emerge . In a 2012 article from Philosophy of the Social Sciences, titled \"The Relevance of the Sociology of Philosophy\", Isaac Reed further expounded on the importance of this interdisciplinary approach, highlighting its potential to deepen our understanding of the dynamics that shape human knowledge and inquiry."}], "pipeline_tag": "token-classification", "model-index": [{"name": "SpanMarker", "results": [{"task": {"type": "token-classification", "name": "Named Entity Recognition"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "eval"}, "metrics": [{"type": "f1", "value": 0.0, "name": "F1"}, {"type": "precision", "value": 0.0, "name": "Precision"}, {"type": "recall", "value": 0.0, "name": "Recall"}]}]}]} | ClovenDoug/span-marker-tiny-bert-ner-citation-grabber | null | [
"span-marker",
"safetensors",
"token-classification",
"ner",
"named-entity-recognition",
"generated_from_span_marker_trainer",
"model-index",
"region:us"
] | null | 2024-04-21T12:24:37+00:00 | [] | [] | TAGS
#span-marker #safetensors #token-classification #ner #named-entity-recognition #generated_from_span_marker_trainer #model-index #region-us
| SpanMarker
==========
This is a SpanMarker model that can be used for Named Entity Recognition.
Model Details
-------------
### Model Description
* Model Type: SpanMarker
* Maximum Sequence Length: 512 tokens
* Maximum Entity Length: 16 words
### Model Sources
* Repository: SpanMarker on GitHub
* Thesis: SpanMarker For Named Entity Recognition
### Model Labels
Evaluation
----------
### Metrics
Uses
----
### Direct Use for Inference
### Downstream Use
You can finetune this model on your own dataset.
Click to expand
Training Details
----------------
### Training Set Metrics
### Training Hyperparameters
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training Results
### Framework Versions
* Python: 3.10.13
* SpanMarker: 1.5.1.dev
* Transformers: 4.39.3
* PyTorch: 2.1.2
* Datasets: 2.16.0
* Tokenizers: 0.15.0
### BibTeX
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"### BibTeX"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | redmojo7/gemma-1.1-7b-it-finetune-palo-alto-network | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:25:40+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gemma #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Imcf1Y3FSatM/weblab-10b-neo-len4096-1500iteration | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:29:20+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_13.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:31:04+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model Details",
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] |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-lora
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6655
- Accuracy: 0.7963
- Precision: 0.7841
- Recall: 0.7963
- Precision Macro: 0.5968
- Recall Macro: 0.6325
- Macro Fpr: 0.0186
- Weighted Fpr: 0.0179
- Weighted Specificity: 0.9749
- Macro Specificity: 0.9847
- Weighted Sensitivity: 0.7963
- Macro Sensitivity: 0.6325
- F1 Micro: 0.7963
- F1 Macro: 0.6074
- F1 Weighted: 0.7859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| No log | 1.0 | 160 | 1.2642 | 0.6313 | 0.5477 | 0.6313 | 0.3009 | 0.3127 | 0.0428 | 0.0400 | 0.9351 | 0.9711 | 0.6313 | 0.3127 | 0.6313 | 0.2941 | 0.5769 |
| No log | 2.0 | 321 | 0.8962 | 0.7119 | 0.6939 | 0.7119 | 0.3937 | 0.4525 | 0.0285 | 0.0281 | 0.9669 | 0.9786 | 0.7119 | 0.4525 | 0.7119 | 0.4107 | 0.6960 |
| No log | 3.0 | 482 | 0.8204 | 0.7196 | 0.6953 | 0.7196 | 0.3974 | 0.4468 | 0.0278 | 0.0271 | 0.9653 | 0.9790 | 0.7196 | 0.4468 | 0.7196 | 0.3998 | 0.6885 |
| 1.2731 | 4.0 | 643 | 0.7519 | 0.7436 | 0.7186 | 0.7436 | 0.4131 | 0.4673 | 0.0244 | 0.0240 | 0.9695 | 0.9809 | 0.7436 | 0.4673 | 0.7436 | 0.4272 | 0.7248 |
| 1.2731 | 5.0 | 803 | 0.7364 | 0.7475 | 0.7524 | 0.7475 | 0.6132 | 0.5050 | 0.0243 | 0.0236 | 0.9679 | 0.9810 | 0.7475 | 0.5050 | 0.7475 | 0.4905 | 0.7286 |
| 1.2731 | 6.0 | 964 | 0.7273 | 0.7514 | 0.7423 | 0.7514 | 0.5784 | 0.5258 | 0.0237 | 0.0231 | 0.9699 | 0.9814 | 0.7514 | 0.5258 | 0.7514 | 0.5150 | 0.7311 |
| 0.7243 | 7.0 | 1125 | 0.6993 | 0.7645 | 0.7478 | 0.7645 | 0.5498 | 0.5565 | 0.0222 | 0.0215 | 0.9721 | 0.9824 | 0.7645 | 0.5565 | 0.7645 | 0.5453 | 0.7538 |
| 0.7243 | 8.0 | 1286 | 0.6952 | 0.7769 | 0.7639 | 0.7769 | 0.5682 | 0.5888 | 0.0207 | 0.0201 | 0.9731 | 0.9833 | 0.7769 | 0.5888 | 0.7769 | 0.5700 | 0.7649 |
| 0.7243 | 9.0 | 1446 | 0.6759 | 0.7823 | 0.7708 | 0.7823 | 0.5764 | 0.5877 | 0.0201 | 0.0195 | 0.9739 | 0.9838 | 0.7823 | 0.5877 | 0.7823 | 0.5699 | 0.7697 |
| 0.6098 | 10.0 | 1607 | 0.6705 | 0.7847 | 0.7720 | 0.7847 | 0.5899 | 0.6176 | 0.0199 | 0.0192 | 0.9732 | 0.9839 | 0.7847 | 0.6176 | 0.7847 | 0.5935 | 0.7724 |
| 0.6098 | 11.0 | 1768 | 0.6794 | 0.7909 | 0.7737 | 0.7909 | 0.5882 | 0.6237 | 0.0193 | 0.0185 | 0.9736 | 0.9843 | 0.7909 | 0.6237 | 0.7909 | 0.5988 | 0.7773 |
| 0.6098 | 12.0 | 1929 | 0.6836 | 0.7909 | 0.7816 | 0.7909 | 0.5973 | 0.6285 | 0.0192 | 0.0185 | 0.9742 | 0.9843 | 0.7909 | 0.6285 | 0.7909 | 0.6034 | 0.7802 |
| 0.5239 | 13.0 | 2089 | 0.6508 | 0.7932 | 0.7783 | 0.7932 | 0.5965 | 0.6273 | 0.0189 | 0.0183 | 0.9738 | 0.9845 | 0.7932 | 0.6273 | 0.7932 | 0.6046 | 0.7821 |
| 0.5239 | 14.0 | 2250 | 0.6588 | 0.7963 | 0.7823 | 0.7963 | 0.5957 | 0.6290 | 0.0186 | 0.0179 | 0.9746 | 0.9847 | 0.7963 | 0.6290 | 0.7963 | 0.6055 | 0.7852 |
| 0.5239 | 14.93 | 2400 | 0.6655 | 0.7963 | 0.7841 | 0.7963 | 0.5968 | 0.6325 | 0.0186 | 0.0179 | 0.9749 | 0.9847 | 0.7963 | 0.6325 | 0.7963 | 0.6074 | 0.7859 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-lora", "results": []}]} | xshubhamx/bart-base-lora | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T12:31:41+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #region-us
| bart-base-lora
==============
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6655
* Accuracy: 0.7963
* Precision: 0.7841
* Recall: 0.7963
* Precision Macro: 0.5968
* Recall Macro: 0.6325
* Macro Fpr: 0.0186
* Weighted Fpr: 0.0179
* Weighted Specificity: 0.9749
* Macro Specificity: 0.9847
* Weighted Sensitivity: 0.7963
* Macro Sensitivity: 0.6325
* F1 Micro: 0.7963
* F1 Macro: 0.6074
* F1 Weighted: 0.7859
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.18.0
* Tokenizers 0.15.1
| [
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] |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-7b-Instruct-hf - bnb 4bits
- Model creator: https://huggingface.co/meta-llama/
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Original model description:
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c. If you institute litigation or other proceedings against Meta or any
entity (including a cross-claim or counterclaim in a lawsuit) alleging that
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continue in full force and effect until terminated in accordance with the
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Sale of Goods does not apply to this Agreement. The courts of California
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Agreement.
USE POLICY
### Llama 2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 2. If you access or use Llama 2, you agree to this Acceptable
Use Policy (“Policy”). The most recent copy of this policy can be found at
[ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
#### Prohibited Uses
We want everyone to use Llama 2 safely and responsibly. You agree you will not
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Please report any violation of this Policy, software “bug,” or other problems
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language:
- code
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [meta-llama/CodeLlama-7b-hf](https://huggingface.co/meta-llama/CodeLlama-7b-hf) | [meta-llama/CodeLlama-7b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Python-hf) | [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf) |
| 13B | [meta-llama/CodeLlama-13b-hf](https://huggingface.co/meta-llama/CodeLlama-13b-hf) | [meta-llama/CodeLlama-13b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Python-hf) | [meta-llama/CodeLlama-13b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Instruct-hf) |
| 34B | [meta-llama/CodeLlama-34b-hf](https://huggingface.co/meta-llama/CodeLlama-34b-hf) | [meta-llama/CodeLlama-34b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf) | [meta-llama/CodeLlama-34b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Instruct-hf) |
| 70B | [meta-llama/CodeLlama-70b-hf](https://huggingface.co/meta-llama/CodeLlama-70b-hf) | [meta-llama/CodeLlama-70b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Python-hf) | [meta-llama/CodeLlama-70b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2308.12950",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T12:32:21+00:00 | [
"2308.12950"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-2308.12950 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| Quantization made by Richard Erkhov.
Github
Discord
Request more models
CodeLlama-7b-Instruct-hf - bnb 4bits
* Model creator: URL
* Original model: URL
Original model description:
---------------------------
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construed under the laws of the State of California without regard to choice
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Sale of Goods does not apply to this Agreement. The courts of California
shall have exclusive jurisdiction of any dispute arising out of this
Agreement.
USE POLICY
### Llama 2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 2. If you access or use Llama 2, you agree to this Acceptable
Use Policy (“Policy”). The most recent copy of this policy can be found at
URL
#### Prohibited Uses
We want everyone to use Llama 2 safely and responsibly. You agree you will not
use, or allow others to use, Llama 2 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
```
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
```
2. Engage in, promote, incite, facilitate, or assist in the planning or
development of activities that present a risk of death or bodily harm to
individuals, including use of Llama 2 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 2 related
to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 2 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
means:
* Reporting issues with the model:
URL
* Reporting risky content generated by the model:
URL
* Reporting bugs and security concerns:
URL
* Reporting violations of the Acceptable Use Policy or unlicensed uses of
Llama: LlamaUseReport@URL
extra\_gated\_fields:
First Name: text
Last Name: text
Date of birth: date\_picker
Country: country
Affiliation: text
geo: ip\_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
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extra\_gated\_button\_content: Submit
language:
* code
pipeline\_tag: text-generation
tags:
* facebook
* meta
* pytorch
* llama
* llama-2
license: llama2
---
Code Llama
==========
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
Model Use
---------
To use this model, please make sure to install transformers:
Model capabilities:
* [x] Code completion.
* [x] Infilling.
* [x] Instructions / chat.
* [ ] Python specialist.
Model Details
-------------
\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
Model Developers Meta
Variations Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
This repository contains the Instruct version of the 7B parameters model.
Input Models input text only.
Output Models generate text only.
Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
Model Dates Code Llama and its variants have been trained between January 2023 and July 2023.
Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
License A custom commercial license is available at: URL
Research Paper More information can be found in the paper "Code Llama: Open Foundation Models for Code" or its arXiv page.
Intended Use
------------
Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
Hardware and Software
---------------------
Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
Carbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
Training Data
-------------
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).
Evaluation Results
------------------
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
Ethical Considerations and Limitations
--------------------------------------
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at URL
| [
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install transformers:\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [x] Infilling.\n* [x] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in three model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B and 34B parameters.\n\n\nThis repository contains the Instruct version of the 7B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and July 2023.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nTraining Data\n-------------\n\n\nAll experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2308.12950 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install transformers:\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [x] Infilling.\n* [x] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in three model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B and 34B parameters.\n\n\nThis repository contains the Instruct version of the 7B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and July 2023.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nTraining Data\n-------------\n\n\nAll experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] |
null | transformers |
# Uploaded model
- **Developed by:** ogdanneedham
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | ogdanneedham/mistral-gs-0.2-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:33:58+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: ogdanneedham
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-7b-Instruct-hf - bnb 8bits
- Model creator: https://huggingface.co/meta-llama/
- Original model: https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf/
Original model description:
---
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### LLAMA 2 COMMUNITY LICENSE AGREEMENT
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USE POLICY
### Llama 2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 2. If you access or use Llama 2, you agree to this Acceptable
Use Policy (“Policy”). The most recent copy of this policy can be found at
[ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
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We want everyone to use Llama 2 safely and responsibly. You agree you will not
use, or allow others to use, Llama 2 to:
1. Violate the law or others’ rights, including to:
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Please report any violation of this Policy, software “bug,” or other problems
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language:
- code
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [meta-llama/CodeLlama-7b-hf](https://huggingface.co/meta-llama/CodeLlama-7b-hf) | [meta-llama/CodeLlama-7b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Python-hf) | [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf) |
| 13B | [meta-llama/CodeLlama-13b-hf](https://huggingface.co/meta-llama/CodeLlama-13b-hf) | [meta-llama/CodeLlama-13b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Python-hf) | [meta-llama/CodeLlama-13b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Instruct-hf) |
| 34B | [meta-llama/CodeLlama-34b-hf](https://huggingface.co/meta-llama/CodeLlama-34b-hf) | [meta-llama/CodeLlama-34b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf) | [meta-llama/CodeLlama-34b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Instruct-hf) |
| 70B | [meta-llama/CodeLlama-70b-hf](https://huggingface.co/meta-llama/CodeLlama-70b-hf) | [meta-llama/CodeLlama-70b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Python-hf) | [meta-llama/CodeLlama-70b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2308.12950",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-21T12:35:39+00:00 | [
"2308.12950"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-2308.12950 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
| Quantization made by Richard Erkhov.
Github
Discord
Request more models
CodeLlama-7b-Instruct-hf - bnb 8bits
* Model creator: URL
* Original model: URL
Original model description:
---------------------------
extra\_gated\_heading: You need to share contact information with Meta to access this model
extra\_gated\_prompt: >-
### LLAMA 2 COMMUNITY LICENSE AGREEMENT
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and modification of the Llama Materials set forth herein.
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the Llama Materials.
b. Subject to Meta's ownership of Llama Materials and derivatives made by or
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construed under the laws of the State of California without regard to choice
of law principles, and the UN Convention on Contracts for the International
Sale of Goods does not apply to this Agreement. The courts of California
shall have exclusive jurisdiction of any dispute arising out of this
Agreement.
USE POLICY
### Llama 2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 2. If you access or use Llama 2, you agree to this Acceptable
Use Policy (“Policy”). The most recent copy of this policy can be found at
URL
#### Prohibited Uses
We want everyone to use Llama 2 safely and responsibly. You agree you will not
use, or allow others to use, Llama 2 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
```
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
```
2. Engage in, promote, incite, facilitate, or assist in the planning or
development of activities that present a risk of death or bodily harm to
individuals, including use of Llama 2 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 2 related
to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 2 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
means:
* Reporting issues with the model:
URL
* Reporting risky content generated by the model:
URL
* Reporting bugs and security concerns:
URL
* Reporting violations of the Acceptable Use Policy or unlicensed uses of
Llama: LlamaUseReport@URL
extra\_gated\_fields:
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language:
* code
pipeline\_tag: text-generation
tags:
* facebook
* meta
* pytorch
* llama
* llama-2
license: llama2
---
Code Llama
==========
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
Model Use
---------
To use this model, please make sure to install transformers:
Model capabilities:
* [x] Code completion.
* [x] Infilling.
* [x] Instructions / chat.
* [ ] Python specialist.
Model Details
-------------
\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
Model Developers Meta
Variations Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
This repository contains the Instruct version of the 7B parameters model.
Input Models input text only.
Output Models generate text only.
Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
Model Dates Code Llama and its variants have been trained between January 2023 and July 2023.
Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
License A custom commercial license is available at: URL
Research Paper More information can be found in the paper "Code Llama: Open Foundation Models for Code" or its arXiv page.
Intended Use
------------
Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
Hardware and Software
---------------------
Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
Carbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
Training Data
-------------
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).
Evaluation Results
------------------
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
Ethical Considerations and Limitations
--------------------------------------
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at URL
| [
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install transformers:\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [x] Infilling.\n* [x] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in three model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B and 34B parameters.\n\n\nThis repository contains the Instruct version of the 7B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and July 2023.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nTraining Data\n-------------\n\n\nAll experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2308.12950 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n",
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install transformers:\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [x] Infilling.\n* [x] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in three model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B and 34B parameters.\n\n\nThis repository contains the Instruct version of the 7B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and July 2023.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nTraining Data\n-------------\n\n\nAll experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] |
text-generation | transformers |
How to use：
------
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "BoyangZ/Llama3-chinese_chat_ft"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a LLM assistant. Users will ask you questions in Chinese, You will answer questions in Chinese"},
{"role": "user", "content": "李白是哪个朝代的人?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
example1
---
example2
---
example3
---
| {"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "text-generation"} | BoyangZ/Llama3-chinese_chat_ft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:36:13+00:00 | [] | [
"zh"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
How to use：
------
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "BoyangZ/Llama3-chinese_chat_ft"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a LLM assistant. Users will ask you questions in Chinese, You will answer questions in Chinese"},
{"role": "user", "content": "李白是哪个朝代的人?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(URL)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(URL(response, skip_special_tokens=True))
example1
---
!image/png
example2
---
!image/png
example3
---
!image/png
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Classifier_with_external_sets_02
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6968
- Accuracy: 0.5034
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.9983 | 289 | 0.6937 | 0.4966 |
| 0.6958 | 2.0 | 579 | 0.6958 | 0.4966 |
| 0.6958 | 2.9983 | 868 | 0.6972 | 0.4966 |
| 0.6845 | 4.0 | 1158 | 0.6931 | 0.5034 |
| 0.6845 | 4.9983 | 1447 | 0.7009 | 0.5034 |
| 0.6548 | 6.0 | 1737 | 0.7251 | 0.5034 |
| 0.6484 | 6.9983 | 2026 | 0.7186 | 0.5034 |
| 0.6484 | 8.0 | 2316 | 0.7049 | 0.5034 |
| 0.6453 | 8.9983 | 2605 | 0.6997 | 0.5034 |
| 0.6453 | 9.9827 | 2890 | 0.6968 | 0.5034 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "Classifier_with_external_sets_02", "results": []}]} | Tensorride/Classifier_with_external_sets_02 | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:36:44+00:00 | [] | [] | TAGS
#transformers #safetensors #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Classifier\_with\_external\_sets\_02
====================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6968
* Accuracy: 0.5034
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | LiuErXiao/vit_lora-1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:38:19+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | domenicrosati/adversarial_loss_lr_1e-5_attack_meta-llama_Llama-2-7b-chat-hf_masked_4_3e-5_1k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:39:09+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6705
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
TASK2_OversampledModel :
precision recall f1-score support
0 1.00 1.00 1.00 3230
1 0.71 0.60 0.65 2037
2 0.39 0.46 0.42 1195
3 0.43 0.47 0.45 1074
accuracy 0.73 7536
macro avg 0.63 0.63 0.63 7536
weighted avg 0.74 0.73 0.73 7536
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "output", "results": []}]} | SumedhJoshi/idk | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:39:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# output
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6705
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
TASK2_OversampledModel :
precision recall f1-score support
0 1.00 1.00 1.00 3230
1 0.71 0.60 0.65 2037
2 0.39 0.46 0.42 1195
3 0.43 0.47 0.45 1074
accuracy 0.73 7536
macro avg 0.63 0.63 0.63 7536
weighted avg 0.74 0.73 0.73 7536
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# output\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6705",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results\n\nTASK2_OversampledModel :\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3230\n 1 0.71 0.60 0.65 2037\n 2 0.39 0.46 0.42 1195\n 3 0.43 0.47 0.45 1074\n\n accuracy 0.73 7536\n macro avg 0.63 0.63 0.63 7536\nweighted avg 0.74 0.73 0.73 7536",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# output\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6705",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results\n\nTASK2_OversampledModel :\n precision recall f1-score support\n\n 0 1.00 1.00 1.00 3230\n 1 0.71 0.60 0.65 2037\n 2 0.39 0.46 0.42 1195\n 3 0.43 0.47 0.45 1074\n\n accuracy 0.73 7536\n macro avg 0.63 0.63 0.63 7536\nweighted avg 0.74 0.73 0.73 7536",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers | **General-Stories-Mistral-7B**
This model is based on my dataset [General-Stories-Collection](https://huggingface.co/datasets/ajibawa-2023/General-Stories-Collection) which has **1.3 million** stories especially meant for General audience.
After an extensive training period spanning over 15 days, this model has been meticulously honed to deliver captivating narratives with broad appeal.
Leveraging a vast synthetic dataset comprising approximately **1.3 million** stories tailored for diverse readership, this model possesses a deep understanding of narrative intricacies and themes.
What sets my model apart is not just its ability to generate stories, but its capacity to evoke emotion, spark imagination, and forge connections with its audience.
I am excited to introduce this powerful tool, ready to spark imagination and entertain readers worldwide with its versatile storytelling capabilities.
As we embark on this exciting journey of AI storytelling, I invite you to explore the endless possibilities my model has to offer. Whether you're a writer seeking inspiration, a reader in search of a captivating tale, or a creative mind eager to push the boundaries of storytelling, my model is here to inspire, entertain, and enrich your literary experience.
Kindly note this is qLoRA version.
**GGUF & Exllama**
GGUF: [Link](https://huggingface.co/bartowski/General-Stories-Mistral-7B-GGUF)
Exllama v2: [Link](https://huggingface.co/bartowski/General-Stories-Mistral-7B-exl2)
Special Thanks to [Bartowski](https://huggingface.co/bartowski) for quantizing this model.
**Training**
Entire dataset was trained on 4 x A100 80GB. For 2 epoch, training took more than **15 Days**. Axolotl codebase was used for training purpose. Entire data is trained on Mistral-7B-v0.1.
**Example Prompt:**
This model uses **ChatML** prompt format.
```
<|im_start|>system
You are a Helpful Assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
You can modify above Prompt as per your requirement.
I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
Thank you for your love & support.
**Example Output**
Example 1
Example 2
Example 3
Example 4
 | {"language": ["en"], "license": "apache-2.0", "tags": ["story", "art", "general audience", "knowledge"], "datasets": ["ajibawa-2023/General-Stories-Collection"]} | ajibawa-2023/General-Stories-Mistral-7B | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"story",
"art",
"general audience",
"knowledge",
"conversational",
"en",
"dataset:ajibawa-2023/General-Stories-Collection",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:40:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #mistral #text-generation #story #art #general audience #knowledge #conversational #en #dataset-ajibawa-2023/General-Stories-Collection #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| General-Stories-Mistral-7B
This model is based on my dataset General-Stories-Collection which has 1.3 million stories especially meant for General audience.
After an extensive training period spanning over 15 days, this model has been meticulously honed to deliver captivating narratives with broad appeal.
Leveraging a vast synthetic dataset comprising approximately 1.3 million stories tailored for diverse readership, this model possesses a deep understanding of narrative intricacies and themes.
What sets my model apart is not just its ability to generate stories, but its capacity to evoke emotion, spark imagination, and forge connections with its audience.
I am excited to introduce this powerful tool, ready to spark imagination and entertain readers worldwide with its versatile storytelling capabilities.
As we embark on this exciting journey of AI storytelling, I invite you to explore the endless possibilities my model has to offer. Whether you're a writer seeking inspiration, a reader in search of a captivating tale, or a creative mind eager to push the boundaries of storytelling, my model is here to inspire, entertain, and enrich your literary experience.
Kindly note this is qLoRA version.
GGUF & Exllama
GGUF: Link
Exllama v2: Link
Special Thanks to Bartowski for quantizing this model.
Training
Entire dataset was trained on 4 x A100 80GB. For 2 epoch, training took more than 15 Days. Axolotl codebase was used for training purpose. Entire data is trained on Mistral-7B-v0.1.
Example Prompt:
This model uses ChatML prompt format.
You can modify above Prompt as per your requirement.
I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
Thank you for your love & support.
Example Output
Example 1
!image/jpeg
Example 2
!image/jpeg
Example 3
!image/jpeg
Example 4
!image/jpeg | [] | [
"TAGS\n#transformers #pytorch #mistral #text-generation #story #art #general audience #knowledge #conversational #en #dataset-ajibawa-2023/General-Stories-Collection #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-7b-Instruct-hf - GGUF
- Model creator: https://huggingface.co/meta-llama/
- Original model: https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [CodeLlama-7b-Instruct-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q2_K.gguf) | Q2_K | 2.36GB |
| [CodeLlama-7b-Instruct-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
| [CodeLlama-7b-Instruct-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ3_S.gguf) | IQ3_S | 2.75GB |
| [CodeLlama-7b-Instruct-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [CodeLlama-7b-Instruct-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ3_M.gguf) | IQ3_M | 2.9GB |
| [CodeLlama-7b-Instruct-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K.gguf) | Q3_K | 3.07GB |
| [CodeLlama-7b-Instruct-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [CodeLlama-7b-Instruct-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [CodeLlama-7b-Instruct-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [CodeLlama-7b-Instruct-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_0.gguf) | Q4_0 | 3.56GB |
| [CodeLlama-7b-Instruct-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [CodeLlama-7b-Instruct-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [CodeLlama-7b-Instruct-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_K.gguf) | Q4_K | 3.8GB |
| [CodeLlama-7b-Instruct-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [CodeLlama-7b-Instruct-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q4_1.gguf) | Q4_1 | 3.95GB |
| [CodeLlama-7b-Instruct-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_0.gguf) | Q5_0 | 4.33GB |
| [CodeLlama-7b-Instruct-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [CodeLlama-7b-Instruct-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_K.gguf) | Q5_K | 4.45GB |
| [CodeLlama-7b-Instruct-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [CodeLlama-7b-Instruct-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q5_1.gguf) | Q5_1 | 4.72GB |
| [CodeLlama-7b-Instruct-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf/blob/main/CodeLlama-7b-Instruct-hf.Q6_K.gguf) | Q6_K | 5.15GB |
Original model description:
---
extra_gated_heading: You need to share contact information with Meta to access this model
extra_gated_prompt: >-
### LLAMA 2 COMMUNITY LICENSE AGREEMENT
"Agreement" means the terms and conditions for use, reproduction, distribution
and modification of the Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation
accompanying Llama 2 distributed by Meta at
https://ai.meta.com/resources/models-and-libraries/llama-downloads/.
"Licensee" or "you" means you, or your employer or any other person or entity
(if you are entering into this Agreement on such person or entity's behalf),
of the age required under applicable laws, rules or regulations to provide
legal consent and that has legal authority to bind your employer or such other
person or entity if you are entering in this Agreement on their behalf.
"Llama 2" means the foundational large language models and software and
algorithms, including machine-learning model code, trained model weights,
inference-enabling code, training-enabling code, fine-tuning enabling code and
other elements of the foregoing distributed by Meta at
ai.meta.com/resources/models-and-libraries/llama-downloads/.
"Llama Materials" means, collectively, Meta's proprietary Llama 2 and
documentation (and any portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or,
if you are an entity, your principal place of business is in the EEA or
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or Switzerland).
By clicking "I Accept" below or by using or distributing any portion or
element of the Llama Materials, you agree to be bound by this Agreement.
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
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property or other rights owned by Meta embodied in the Llama Materials to
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b. Redistribution and Use.
i. If you distribute or make the Llama Materials, or any derivative works
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### Llama 2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 2. If you access or use Llama 2, you agree to this Acceptable
Use Policy (“Policy”). The most recent copy of this policy can be found at
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We want everyone to use Llama 2 safely and responsibly. You agree you will not
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7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
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Please report any violation of this Policy, software “bug,” or other problems
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language:
- code
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [meta-llama/CodeLlama-7b-hf](https://huggingface.co/meta-llama/CodeLlama-7b-hf) | [meta-llama/CodeLlama-7b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Python-hf) | [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf) |
| 13B | [meta-llama/CodeLlama-13b-hf](https://huggingface.co/meta-llama/CodeLlama-13b-hf) | [meta-llama/CodeLlama-13b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Python-hf) | [meta-llama/CodeLlama-13b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Instruct-hf) |
| 34B | [meta-llama/CodeLlama-34b-hf](https://huggingface.co/meta-llama/CodeLlama-34b-hf) | [meta-llama/CodeLlama-34b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf) | [meta-llama/CodeLlama-34b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Instruct-hf) |
| 70B | [meta-llama/CodeLlama-70b-hf](https://huggingface.co/meta-llama/CodeLlama-70b-hf) | [meta-llama/CodeLlama-70b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Python-hf) | [meta-llama/CodeLlama-70b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/meta-llama_-_CodeLlama-7b-Instruct-hf-gguf | null | [
"gguf",
"arxiv:2308.12950",
"region:us"
] | null | 2024-04-21T12:42:40+00:00 | [
"2308.12950"
] | [] | TAGS
#gguf #arxiv-2308.12950 #region-us
| Quantization made by Richard Erkhov.
Github
Discord
Request more models
CodeLlama-7b-Instruct-hf - GGUF
* Model creator: URL
* Original model: URL
Name: CodeLlama-7b-Instruct-hf.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.36GB
Name: CodeLlama-7b-Instruct-hf.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.6GB
Name: CodeLlama-7b-Instruct-hf.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.75GB
Name: CodeLlama-7b-Instruct-hf.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.75GB
Name: CodeLlama-7b-Instruct-hf.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 2.9GB
Name: CodeLlama-7b-Instruct-hf.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.07GB
Name: CodeLlama-7b-Instruct-hf.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.07GB
Name: CodeLlama-7b-Instruct-hf.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.35GB
Name: CodeLlama-7b-Instruct-hf.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.4GB
Name: CodeLlama-7b-Instruct-hf.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.56GB
Name: CodeLlama-7b-Instruct-hf.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.58GB
Name: CodeLlama-7b-Instruct-hf.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.59GB
Name: CodeLlama-7b-Instruct-hf.Q4\_K.gguf, Quant method: Q4\_K, Size: 3.8GB
Name: CodeLlama-7b-Instruct-hf.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 3.8GB
Name: CodeLlama-7b-Instruct-hf.Q4\_1.gguf, Quant method: Q4\_1, Size: 3.95GB
Name: CodeLlama-7b-Instruct-hf.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.33GB
Name: CodeLlama-7b-Instruct-hf.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.33GB
Name: CodeLlama-7b-Instruct-hf.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.45GB
Name: CodeLlama-7b-Instruct-hf.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.45GB
Name: CodeLlama-7b-Instruct-hf.Q5\_1.gguf, Quant method: Q5\_1, Size: 4.72GB
Name: CodeLlama-7b-Instruct-hf.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.15GB
Original model description:
---------------------------
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### LLAMA 2 COMMUNITY LICENSE AGREEMENT
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MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS"
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WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,
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THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE
LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
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UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,
PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST
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PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE
POSSIBILITY OF ANY OF THE FOREGOING.
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a. No trademark licenses are granted under this Agreement, and in connection
with the Llama Materials, neither Meta nor Licensee may use any name or mark
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7. Governing Law and Jurisdiction. This Agreement will be governed and
construed under the laws of the State of California without regard to choice
of law principles, and the UN Convention on Contracts for the International
Sale of Goods does not apply to this Agreement. The courts of California
shall have exclusive jurisdiction of any dispute arising out of this
Agreement.
USE POLICY
### Llama 2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
including Llama 2. If you access or use Llama 2, you agree to this Acceptable
Use Policy (“Policy”). The most recent copy of this policy can be found at
URL
#### Prohibited Uses
We want everyone to use Llama 2 safely and responsibly. You agree you will not
use, or allow others to use, Llama 2 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
```
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
```
2. Engage in, promote, incite, facilitate, or assist in the planning or
development of activities that present a risk of death or bodily harm to
individuals, including use of Llama 2 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 2 related
to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 2 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
means:
* Reporting issues with the model:
URL
* Reporting risky content generated by the model:
URL
* Reporting bugs and security concerns:
URL
* Reporting violations of the Acceptable Use Policy or unlicensed uses of
Llama: LlamaUseReport@URL
extra\_gated\_fields:
First Name: text
Last Name: text
Date of birth: date\_picker
Country: country
Affiliation: text
geo: ip\_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra\_gated\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.
extra\_gated\_button\_content: Submit
language:
* code
pipeline\_tag: text-generation
tags:
* facebook
* meta
* pytorch
* llama
* llama-2
license: llama2
---
Code Llama
==========
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
Model Use
---------
To use this model, please make sure to install transformers:
Model capabilities:
* [x] Code completion.
* [x] Infilling.
* [x] Instructions / chat.
* [ ] Python specialist.
Model Details
-------------
\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
Model Developers Meta
Variations Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
This repository contains the Instruct version of the 7B parameters model.
Input Models input text only.
Output Models generate text only.
Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
Model Dates Code Llama and its variants have been trained between January 2023 and July 2023.
Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
License A custom commercial license is available at: URL
Research Paper More information can be found in the paper "Code Llama: Open Foundation Models for Code" or its arXiv page.
Intended Use
------------
Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
Hardware and Software
---------------------
Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
Carbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
Training Data
-------------
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).
Evaluation Results
------------------
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
Ethical Considerations and Limitations
--------------------------------------
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at URL
| [
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install transformers:\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [x] Infilling.\n* [x] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in three model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B and 34B parameters.\n\n\nThis repository contains the Instruct version of the 7B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and July 2023.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nTraining Data\n-------------\n\n\nAll experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] | [
"TAGS\n#gguf #arxiv-2308.12950 #region-us \n",
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install transformers:\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [x] Infilling.\n* [x] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in three model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B and 34B parameters.\n\n\nThis repository contains the Instruct version of the 7B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and July 2023.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nTraining Data\n-------------\n\n\nAll experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the research paper for details).\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] |
null | null | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
---
# See complete release notes here:
English: https://nieta-art.feishu.cn/wiki/PpwqwVDzjiNE5kkUhRtcEsn6nmh
Chinese: https://nieta-art.feishu.cn/wiki/ARWLw99w7ikjaSkoV99cxmsOn5g
Civitai: https://civitai.com/models/410737/neta-art-xl
# I. Overview
- Introducing Neta Art XL V1.0, the easiest-to-use SDXL Anime model so far.
- Keywords: Best Character Coverage, Vivid storytelling, Diverse styles, Stable anatomy.
- Major motivation:
- Better stability and anatomy for character visual storytelling purpose:
- Ordered prompt guide for model to easier follow prompts;
- A very good balance between better knowledge and stability.
- Maintain a high ceiling standard for aesthetics across versatile anime art styles, while keeping the baseline of output appealing for general users.
- Less loras for characters / styles / artists, so we make better use of static model acceleration techniques.
### Prompting Guide
To avoid possible ambiguity in text prompt, and leave room for very complicated scene such as multi-character, we found enforcing an ordering in prompts leads to better instruct-following behaviors (Learn from NAI3 / Animagine3 / AIDXL). Specifically, we use the following order in Neta Art XL:
Tag Order: subject (1boy / 1girl) -> character (a girl named frieren from sousou no frieren series) -> Artist trigger (by xxx) -> race (elf) -> composition (cowboy shot) -> style (impasto style) -> theme (fantasy theme) -> main environment (in the forest, at day) -> background (gradient background) -> action (sitting on ground) -> expression (is expressionless) -> main characteristics (white hair) -> body characteristics (twintails, green eyes, parted lip) -> clothing (wearing a white dress) -> clothing accessories (frills) -> other items (a cat) -> secondary environment (grass, sunshine) -> aesthetics (beautiful color, detailed, aesthetic) -> quality ((best quality:1.3))
More examples:
```[{
"prompt": "1girl, solo, character: lucy, white background, cowboy shot, hip cutout, sad expressions, pantyhose, best quality, beautiful color, by bigroll"
},
{
"prompt": "(detailed features, flat color:1.2), (lineart, flat-pasto:0.3),The red giant panda holds its bamboo and falls asleep on the water, best quality, "
},
{
"prompt": "gufeng, 2.5d, impasto, old withered vines and ancient trees, a murder of crows descending into darkness, a small bridge over gently flowing water, quaint houses nestled by the river, best quality, masterpiece, highres, "
},
{
"prompt": "aesthetic, detailed, beautiful color, amazing quality, best quality, high quality, cinematic quality, LUT, fine texture, crisp detail, by rella, by_dkxlek, by makoto shinkai, ayanami_rei, beautiful, parted lips, detailed face, detailed eyes, Large eyes, deep crimson eyes, introspective eyes, eyes designed by Ilya Kuvshinov style, (detailed long hair), blue hair, white hair, (holographic hair), multicolored hair, glow hair, transparent hair, floating hair, wind effect, sad expressions, lonely, melancholy atmosphere, subtle grace, elegance, ethereal beauty, transparent, clarity, from side, looking at viewer, off shoulder, bare shoulders, splashing collarbone, strapless, white dress, wet dress, standing in lake, Calm Lake, water, White Lily, petals, barefoot, ripples, Misty Forest, soft shadows, (clear blue sky), cloud, cloudy sky, starry sky, particle, feather, [moon], (high-resolution sci-fi landscape:1.3), post-human landscapes, thousands of years in the future, Earth, overgrown cities, reclaimed by nature, futuristic decay, ancient modern buildings, high-resolution ruins, (rust in ruins, collapsed building), [2.5d:celluloid:0.6], Depth of Field, sharp focus, bokeh,"
},
{
"prompt": "(from side:1.0), (Red long hair:1.0), (Hair adorned with fine pearls:1.0), (sacred:1.0), (Blue light gauze dress:1.0), (🌊:1.1), (Play the transparent harp:1.2), (Fragmented light dots:1.0), (Dawn:1.0), (fluorescence:0.8), (Crystal and transparent:1.0), (Flowing light and overflowing colors:1.0), (sit:1.0), (Close range:1.0), , (by wlop:0.6), (impasto, oil painting:1.2), detailed features, pseudo-impasto, best quality, (detailed features, flat color:1.2), (lineart, flat-pasto:0.3)"
}]```
Negative prompts: (worst quality:1.3), low quality, lowres, messy, abstract, ugly, disfigured, bad anatomy, draft, deformed hands, fused fingers, signature, text, multi views
Sampler: Eular a normal as default, 28+ steps recommended.
One additional merit of Neta Art XL is that it supports a very wide range of CFGs (5 - 20 compared to 7 - 9 of previous models). While we empirically found higher CFG leads to more details and higher contrast, generally CFG 9 - 14 (important!) can be used for best results.
# II. Highlight: Style Versatility
We carefully selected 13 style keys with good orthogonality and are commonly used in many scenarios, justified by usage data from Nieta AI (30M+ generations).
Having orthogonal styles means each style is effectively different from the others, allowing you to easily combine and create new styles without interference.
impasto
Please refer to https://civitai.com/models/124189/anime-illust-diffusion-xl for a complete list of supported artists.
# III. Expression, Posing, and Camera Angles
# IV. Multi-Character Scenes
# V. Text & Typography
# VI. Training
- Data annotation combining multiple sources (Original prompt, CogVLM captions, WaifuTagger tags)
- Post-processing techniques like semantic deduplication and hierarchical tag organization
1. Semantic Deduplication: This removed redundant tags by intelligently detecting when a higher-level tag (e.g. very long hair) semantically covered a lower-level one (e.g. long hair).
2. Tag Layering Algorithm: Tags were organized into hierarchical layers based on their priorities and related semantics (eg. by wlop influence the whole picture styling, while frills influence a small fraction). More dominant tags were placed in higher layers to prioritize their influence during training.
- Using high-quality regularization data from AIDXL: High-quality regular datasets with "best" and "amazing" quality ratings from AIDXL. These datasets are manually selected and come with detailed annotations and natural language descriptions.
- Finetuning on more knowledgeable base models like AAM, blending with AnimagineXL 3.1 Character Knowledge.
### Challenges Faced:
- Imbalance in learning different styles
- Poor generalization for some styles to diverse scenes
- Lack of details/texture in generations
- Trigger word overlap with base model knowledge
### Solutions Explored:
- Data reweighting to balance style learning, and supplement diverse data per style.
- Tuning sampling hyperparameters like minimum gamma and rectified flow. Rectified Flow is a training parameter that increases the sampling frequency in the middle time steps but weakens the weight of the model's learning ability for small noises in the low time steps. This technique helps to improve the model's ability to restore styles but requires the use of a knowledge-rich base model.
- Randomizing / drop off trigger words during training.
# VII. Evaluation
Nine models are evaluated using 16 styles (some unique to Neta Art, thus biased :P) and 80 prompts. Each prompt generates 3 samples with different aspect ratios, resulting in an XYZ plot (Generated from https://github.com/talesofai/comfyui-browser).
XYZPlot of 3,840 samples, just for one-time evaluation. Here's an online Example.
Evaluation is done on a 10-point scale across four axes under objective counting rules:
1. Prompt Following: Points deducted for every 5 images unrelated to the prompt.
2. Stability: Points deducted for distortions or breakages in heads, hands, feet, or composition.
3. Diversity: Points deducted for poor style restoration within each style column.
4. Generalizability: Points deducted for poor semantic generalization within each style column.
Additionally, 10 top model trainers provide subjective scores for Aesthetics, with an average calculated for each model.
# VIII. License
- Developed with ❤️ by: Neta.art Lab - https://civitai.com/user/nieta_art
- In collaboration with:
- Euge: https://civitai.com/user/Euge_
- 汤人烂: https://space.bilibili.com/8594480
- Chenkin: https://civitai.com/user/Chenkin
- Bo Dai: https://daibo.info/
- Thanks to:
- https://blog.novelai.net/introducing-novelai-diffusion-anime-v3-6d00d1c118c3
- https://cagliostrolab.net/posts/animagine-xl-v3-release
- https://civitai.com/models/269232/aam-xl-anime-mix
- https://civitai.com/models/124189/anime-illust-diffusion-xl
- https://github.com/deepghs/waifuc
-
- Model type: Diffusion-based text-to-image generative model
- License: We merged 0.05 CLIP and 0.15 UNet input layers from Animagine 3.1, thus Fair AI Public License 1.0-SD
# IX. Conclusion and Future Work
Shortcomings:
1. Some characters are underfitted.
2. Styles are not activated well with long prompts.
3. Certain styles appear grayish at low CFG and short prompts. Partly explained in https://civitai.com/articles/4969.
Future Work:
- Prepare larger training sets and more knowledge-based data to improve character, style, and detail handling.
- Welcome others to join discussions, provide suggestions, and contribute to model advancement.
_Neta Art XL 2.0 is on the way._
Stay tuned with us, and test our product for FREE: http://neta.art/
Discord: https://discord.gg/AtRtbe9W8w
Twitter: https://twitter.com/netaart_ai
Civitai：https://civitai.com/user/neta_art | {"license": "other", "license_name": "faipl-1.0-sd", "license_link": "https://freedevproject.org/faipl-1.0-sd/"} | neta-art/neta-art-xl-1.0 | null | [
"license:other",
"region:us"
] | null | 2024-04-21T12:42:54+00:00 | [] | [] | TAGS
#license-other #region-us
| ---
license: other
license_name: faipl-1.0-sd
license_link: URL
---
# See complete release notes here:
English: URL
Chinese: URL
Civitai: URL
# I. Overview
- Introducing Neta Art XL V1.0, the easiest-to-use SDXL Anime model so far.
- Keywords: Best Character Coverage, Vivid storytelling, Diverse styles, Stable anatomy.
- Major motivation:
- Better stability and anatomy for character visual storytelling purpose:
- Ordered prompt guide for model to easier follow prompts;
- A very good balance between better knowledge and stability.
- Maintain a high ceiling standard for aesthetics across versatile anime art styles, while keeping the baseline of output appealing for general users.
- Less loras for characters / styles / artists, so we make better use of static model acceleration techniques.
### Prompting Guide
To avoid possible ambiguity in text prompt, and leave room for very complicated scene such as multi-character, we found enforcing an ordering in prompts leads to better instruct-following behaviors (Learn from NAI3 / Animagine3 / AIDXL). Specifically, we use the following order in Neta Art XL:
Tag Order: subject (1boy / 1girl) -> character (a girl named frieren from sousou no frieren series) -> Artist trigger (by xxx) -> race (elf) -> composition (cowboy shot) -> style (impasto style) -> theme (fantasy theme) -> main environment (in the forest, at day) -> background (gradient background) -> action (sitting on ground) -> expression (is expressionless) -> main characteristics (white hair) -> body characteristics (twintails, green eyes, parted lip) -> clothing (wearing a white dress) -> clothing accessories (frills) -> other items (a cat) -> secondary environment (grass, sunshine) -> aesthetics (beautiful color, detailed, aesthetic) -> quality ((best quality:1.3))
More examples:
Negative prompts: (worst quality:1.3), low quality, lowres, messy, abstract, ugly, disfigured, bad anatomy, draft, deformed hands, fused fingers, signature, text, multi views
Sampler: Eular a normal as default, 28+ steps recommended.
One additional merit of Neta Art XL is that it supports a very wide range of CFGs (5 - 20 compared to 7 - 9 of previous models). While we empirically found higher CFG leads to more details and higher contrast, generally CFG 9 - 14 (important!) can be used for best results.
# II. Highlight: Style Versatility
We carefully selected 13 style keys with good orthogonality and are commonly used in many scenarios, justified by usage data from Nieta AI (30M+ generations).
Having orthogonal styles means each style is effectively different from the others, allowing you to easily combine and create new styles without interference.
impasto
Please refer to URL for a complete list of supported artists.
# III. Expression, Posing, and Camera Angles
# IV. Multi-Character Scenes
# V. Text & Typography
# VI. Training
- Data annotation combining multiple sources (Original prompt, CogVLM captions, WaifuTagger tags)
- Post-processing techniques like semantic deduplication and hierarchical tag organization
1. Semantic Deduplication: This removed redundant tags by intelligently detecting when a higher-level tag (e.g. very long hair) semantically covered a lower-level one (e.g. long hair).
2. Tag Layering Algorithm: Tags were organized into hierarchical layers based on their priorities and related semantics (eg. by wlop influence the whole picture styling, while frills influence a small fraction). More dominant tags were placed in higher layers to prioritize their influence during training.
- Using high-quality regularization data from AIDXL: High-quality regular datasets with "best" and "amazing" quality ratings from AIDXL. These datasets are manually selected and come with detailed annotations and natural language descriptions.
- Finetuning on more knowledgeable base models like AAM, blending with AnimagineXL 3.1 Character Knowledge.
### Challenges Faced:
- Imbalance in learning different styles
- Poor generalization for some styles to diverse scenes
- Lack of details/texture in generations
- Trigger word overlap with base model knowledge
### Solutions Explored:
- Data reweighting to balance style learning, and supplement diverse data per style.
- Tuning sampling hyperparameters like minimum gamma and rectified flow. Rectified Flow is a training parameter that increases the sampling frequency in the middle time steps but weakens the weight of the model's learning ability for small noises in the low time steps. This technique helps to improve the model's ability to restore styles but requires the use of a knowledge-rich base model.
- Randomizing / drop off trigger words during training.
# VII. Evaluation
Nine models are evaluated using 16 styles (some unique to Neta Art, thus biased :P) and 80 prompts. Each prompt generates 3 samples with different aspect ratios, resulting in an XYZ plot (Generated from URL
XYZPlot of 3,840 samples, just for one-time evaluation. Here's an online Example.
Evaluation is done on a 10-point scale across four axes under objective counting rules:
1. Prompt Following: Points deducted for every 5 images unrelated to the prompt.
2. Stability: Points deducted for distortions or breakages in heads, hands, feet, or composition.
3. Diversity: Points deducted for poor style restoration within each style column.
4. Generalizability: Points deducted for poor semantic generalization within each style column.
Additionally, 10 top model trainers provide subjective scores for Aesthetics, with an average calculated for each model.
# VIII. License
- Developed with ️ by: URL Lab - URL
- In collaboration with:
- Euge: URL
- 汤人烂: URL
- Chenkin: URL
- Bo Dai: URL
- Thanks to:
- URL
- URL
- URL
- URL
- URL
-
- Model type: Diffusion-based text-to-image generative model
- License: We merged 0.05 CLIP and 0.15 UNet input layers from Animagine 3.1, thus Fair AI Public License 1.0-SD
# IX. Conclusion and Future Work
Shortcomings:
1. Some characters are underfitted.
2. Styles are not activated well with long prompts.
3. Certain styles appear grayish at low CFG and short prompts. Partly explained in URL
Future Work:
- Prepare larger training sets and more knowledge-based data to improve character, style, and detail handling.
- Welcome others to join discussions, provide suggestions, and contribute to model advancement.
_Neta Art XL 2.0 is on the way._
Stay tuned with us, and test our product for FREE: URL
Discord: URL
Twitter: URL
Civitai：URL | [
"# See complete release notes here:\n\nEnglish: URL\n\nChinese: URL\n\nCivitai: URL",
"# I. Overview\n- Introducing Neta Art XL V1.0, the easiest-to-use SDXL Anime model so far.\n- Keywords: Best Character Coverage, Vivid storytelling, Diverse styles, Stable anatomy.\n- Major motivation: \n - Better stability and anatomy for character visual storytelling purpose:\n - Ordered prompt guide for model to easier follow prompts;\n - A very good balance between better knowledge and stability.\n - Maintain a high ceiling standard for aesthetics across versatile anime art styles, while keeping the baseline of output appealing for general users.\n - Less loras for characters / styles / artists, so we make better use of static model acceleration techniques.",
"### Prompting Guide\nTo avoid possible ambiguity in text prompt, and leave room for very complicated scene such as multi-character, we found enforcing an ordering in prompts leads to better instruct-following behaviors (Learn from NAI3 / Animagine3 / AIDXL). Specifically, we use the following order in Neta Art XL:\n\nTag Order: subject (1boy / 1girl) -> character (a girl named frieren from sousou no frieren series) -> Artist trigger (by xxx) -> race (elf) -> composition (cowboy shot) -> style (impasto style) -> theme (fantasy theme) -> main environment (in the forest, at day) -> background (gradient background) -> action (sitting on ground) -> expression (is expressionless) -> main characteristics (white hair) -> body characteristics (twintails, green eyes, parted lip) -> clothing (wearing a white dress) -> clothing accessories (frills) -> other items (a cat) -> secondary environment (grass, sunshine) -> aesthetics (beautiful color, detailed, aesthetic) -> quality ((best quality:1.3))\n\nMore examples: \n\n\nNegative prompts: (worst quality:1.3), low quality, lowres, messy, abstract, ugly, disfigured, bad anatomy, draft, deformed hands, fused fingers, signature, text, multi views\n\nSampler: Eular a normal as default, 28+ steps recommended.\n\nOne additional merit of Neta Art XL is that it supports a very wide range of CFGs (5 - 20 compared to 7 - 9 of previous models). While we empirically found higher CFG leads to more details and higher contrast, generally CFG 9 - 14 (important!) can be used for best results.",
"# II. Highlight: Style Versatility\n\nWe carefully selected 13 style keys with good orthogonality and are commonly used in many scenarios, justified by usage data from Nieta AI (30M+ generations).\nHaving orthogonal styles means each style is effectively different from the others, allowing you to easily combine and create new styles without interference.\nimpasto\n\nPlease refer to URL for a complete list of supported artists.",
"# III. Expression, Posing, and Camera Angles",
"# IV. Multi-Character Scenes",
"# V. Text & Typography",
"# VI. Training\n- Data annotation combining multiple sources (Original prompt, CogVLM captions, WaifuTagger tags)\n- Post-processing techniques like semantic deduplication and hierarchical tag organization\n 1. Semantic Deduplication: This removed redundant tags by intelligently detecting when a higher-level tag (e.g. very long hair) semantically covered a lower-level one (e.g. long hair).\n 2. Tag Layering Algorithm: Tags were organized into hierarchical layers based on their priorities and related semantics (eg. by wlop influence the whole picture styling, while frills influence a small fraction). More dominant tags were placed in higher layers to prioritize their influence during training.\n\n- Using high-quality regularization data from AIDXL: High-quality regular datasets with \"best\" and \"amazing\" quality ratings from AIDXL. These datasets are manually selected and come with detailed annotations and natural language descriptions. \n- Finetuning on more knowledgeable base models like AAM, blending with AnimagineXL 3.1 Character Knowledge.",
"### Challenges Faced:\n- Imbalance in learning different styles\n- Poor generalization for some styles to diverse scenes\n- Lack of details/texture in generations\n- Trigger word overlap with base model knowledge",
"### Solutions Explored:\n- Data reweighting to balance style learning, and supplement diverse data per style.\n- Tuning sampling hyperparameters like minimum gamma and rectified flow. Rectified Flow is a training parameter that increases the sampling frequency in the middle time steps but weakens the weight of the model's learning ability for small noises in the low time steps. This technique helps to improve the model's ability to restore styles but requires the use of a knowledge-rich base model.\n- Randomizing / drop off trigger words during training.",
"# VII. Evaluation\nNine models are evaluated using 16 styles (some unique to Neta Art, thus biased :P) and 80 prompts. Each prompt generates 3 samples with different aspect ratios, resulting in an XYZ plot (Generated from URL \n\nXYZPlot of 3,840 samples, just for one-time evaluation. Here's an online Example.\n\nEvaluation is done on a 10-point scale across four axes under objective counting rules:\n1. Prompt Following: Points deducted for every 5 images unrelated to the prompt.\n2. Stability: Points deducted for distortions or breakages in heads, hands, feet, or composition.\n3. Diversity: Points deducted for poor style restoration within each style column.\n4. Generalizability: Points deducted for poor semantic generalization within each style column.\n\nAdditionally, 10 top model trainers provide subjective scores for Aesthetics, with an average calculated for each model.",
"# VIII. License\n\n- Developed with ️ by: URL Lab - URL\n- In collaboration with: \n - Euge: URL\n - 汤人烂: URL\n - Chenkin: URL\n - Bo Dai: URL\n- Thanks to:\n - URL\n - URL\n - URL\n - URL\n - URL\n - \n- Model type: Diffusion-based text-to-image generative model\n- License: We merged 0.05 CLIP and 0.15 UNet input layers from Animagine 3.1, thus Fair AI Public License 1.0-SD",
"# IX. Conclusion and Future Work\nShortcomings:\n1. Some characters are underfitted.\n2. Styles are not activated well with long prompts.\n3. Certain styles appear grayish at low CFG and short prompts. Partly explained in URL\nFuture Work:\n- Prepare larger training sets and more knowledge-based data to improve character, style, and detail handling.\n- Welcome others to join discussions, provide suggestions, and contribute to model advancement.\n\n_Neta Art XL 2.0 is on the way._\n\nStay tuned with us, and test our product for FREE: URL\n\nDiscord: URL\n\nTwitter: URL\n\nCivitai：URL"
] | [
"TAGS\n#license-other #region-us \n",
"# See complete release notes here:\n\nEnglish: URL\n\nChinese: URL\n\nCivitai: URL",
"# I. Overview\n- Introducing Neta Art XL V1.0, the easiest-to-use SDXL Anime model so far.\n- Keywords: Best Character Coverage, Vivid storytelling, Diverse styles, Stable anatomy.\n- Major motivation: \n - Better stability and anatomy for character visual storytelling purpose:\n - Ordered prompt guide for model to easier follow prompts;\n - A very good balance between better knowledge and stability.\n - Maintain a high ceiling standard for aesthetics across versatile anime art styles, while keeping the baseline of output appealing for general users.\n - Less loras for characters / styles / artists, so we make better use of static model acceleration techniques.",
"### Prompting Guide\nTo avoid possible ambiguity in text prompt, and leave room for very complicated scene such as multi-character, we found enforcing an ordering in prompts leads to better instruct-following behaviors (Learn from NAI3 / Animagine3 / AIDXL). Specifically, we use the following order in Neta Art XL:\n\nTag Order: subject (1boy / 1girl) -> character (a girl named frieren from sousou no frieren series) -> Artist trigger (by xxx) -> race (elf) -> composition (cowboy shot) -> style (impasto style) -> theme (fantasy theme) -> main environment (in the forest, at day) -> background (gradient background) -> action (sitting on ground) -> expression (is expressionless) -> main characteristics (white hair) -> body characteristics (twintails, green eyes, parted lip) -> clothing (wearing a white dress) -> clothing accessories (frills) -> other items (a cat) -> secondary environment (grass, sunshine) -> aesthetics (beautiful color, detailed, aesthetic) -> quality ((best quality:1.3))\n\nMore examples: \n\n\nNegative prompts: (worst quality:1.3), low quality, lowres, messy, abstract, ugly, disfigured, bad anatomy, draft, deformed hands, fused fingers, signature, text, multi views\n\nSampler: Eular a normal as default, 28+ steps recommended.\n\nOne additional merit of Neta Art XL is that it supports a very wide range of CFGs (5 - 20 compared to 7 - 9 of previous models). While we empirically found higher CFG leads to more details and higher contrast, generally CFG 9 - 14 (important!) can be used for best results.",
"# II. Highlight: Style Versatility\n\nWe carefully selected 13 style keys with good orthogonality and are commonly used in many scenarios, justified by usage data from Nieta AI (30M+ generations).\nHaving orthogonal styles means each style is effectively different from the others, allowing you to easily combine and create new styles without interference.\nimpasto\n\nPlease refer to URL for a complete list of supported artists.",
"# III. Expression, Posing, and Camera Angles",
"# IV. Multi-Character Scenes",
"# V. Text & Typography",
"# VI. Training\n- Data annotation combining multiple sources (Original prompt, CogVLM captions, WaifuTagger tags)\n- Post-processing techniques like semantic deduplication and hierarchical tag organization\n 1. Semantic Deduplication: This removed redundant tags by intelligently detecting when a higher-level tag (e.g. very long hair) semantically covered a lower-level one (e.g. long hair).\n 2. Tag Layering Algorithm: Tags were organized into hierarchical layers based on their priorities and related semantics (eg. by wlop influence the whole picture styling, while frills influence a small fraction). More dominant tags were placed in higher layers to prioritize their influence during training.\n\n- Using high-quality regularization data from AIDXL: High-quality regular datasets with \"best\" and \"amazing\" quality ratings from AIDXL. These datasets are manually selected and come with detailed annotations and natural language descriptions. \n- Finetuning on more knowledgeable base models like AAM, blending with AnimagineXL 3.1 Character Knowledge.",
"### Challenges Faced:\n- Imbalance in learning different styles\n- Poor generalization for some styles to diverse scenes\n- Lack of details/texture in generations\n- Trigger word overlap with base model knowledge",
"### Solutions Explored:\n- Data reweighting to balance style learning, and supplement diverse data per style.\n- Tuning sampling hyperparameters like minimum gamma and rectified flow. Rectified Flow is a training parameter that increases the sampling frequency in the middle time steps but weakens the weight of the model's learning ability for small noises in the low time steps. This technique helps to improve the model's ability to restore styles but requires the use of a knowledge-rich base model.\n- Randomizing / drop off trigger words during training.",
"# VII. Evaluation\nNine models are evaluated using 16 styles (some unique to Neta Art, thus biased :P) and 80 prompts. Each prompt generates 3 samples with different aspect ratios, resulting in an XYZ plot (Generated from URL \n\nXYZPlot of 3,840 samples, just for one-time evaluation. Here's an online Example.\n\nEvaluation is done on a 10-point scale across four axes under objective counting rules:\n1. Prompt Following: Points deducted for every 5 images unrelated to the prompt.\n2. Stability: Points deducted for distortions or breakages in heads, hands, feet, or composition.\n3. Diversity: Points deducted for poor style restoration within each style column.\n4. Generalizability: Points deducted for poor semantic generalization within each style column.\n\nAdditionally, 10 top model trainers provide subjective scores for Aesthetics, with an average calculated for each model.",
"# VIII. License\n\n- Developed with ️ by: URL Lab - URL\n- In collaboration with: \n - Euge: URL\n - 汤人烂: URL\n - Chenkin: URL\n - Bo Dai: URL\n- Thanks to:\n - URL\n - URL\n - URL\n - URL\n - URL\n - \n- Model type: Diffusion-based text-to-image generative model\n- License: We merged 0.05 CLIP and 0.15 UNet input layers from Animagine 3.1, thus Fair AI Public License 1.0-SD",
"# IX. Conclusion and Future Work\nShortcomings:\n1. Some characters are underfitted.\n2. Styles are not activated well with long prompts.\n3. Certain styles appear grayish at low CFG and short prompts. Partly explained in URL\nFuture Work:\n- Prepare larger training sets and more knowledge-based data to improve character, style, and detail handling.\n- Welcome others to join discussions, provide suggestions, and contribute to model advancement.\n\n_Neta Art XL 2.0 is on the way._\n\nStay tuned with us, and test our product for FREE: URL\n\nDiscord: URL\n\nTwitter: URL\n\nCivitai：URL"
] |
text-generation | transformers |
# mlx-community/Llama-3-Smaug-8B-4bit
This model was converted to MLX format from [`abacusai/Llama-3-Smaug-8B`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/abacusai/Llama-3-Smaug-8B) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3-Smaug-8B-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "llama2", "library_name": "transformers", "tags": ["mlx"]} | mlx-community/Llama-3-Smaug-8B-4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mlx",
"conversational",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:43:05+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mlx #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mlx-community/Llama-3-Smaug-8B-4bit
This model was converted to MLX format from ['abacusai/Llama-3-Smaug-8B']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/Llama-3-Smaug-8B-4bit\nThis model was converted to MLX format from ['abacusai/Llama-3-Smaug-8B']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mlx #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mlx-community/Llama-3-Smaug-8B-4bit\nThis model was converted to MLX format from ['abacusai/Llama-3-Smaug-8B']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-generation | transformers |
# Model Card for Mixtral-8x22B-Instruct-v0.1
The Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1).
## Run the model
```python
from transformers import AutoModelForCausalLM
from mistral_common.protocol.instruct.messages import (
AssistantMessage,
UserMessage,
)
from mistral_common.protocol.instruct.tool_calls import (
Tool,
Function,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
device = "cuda" # the device to load the model onto
tokenizer_v3 = MistralTokenizer.v3()
mistral_query = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris"),
],
model="test",
)
encodeds = tokenizer_v3.encode_chat_completion(mistral_query).tokens
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
sp_tokenizer = tokenizer_v3.instruct_tokenizer.tokenizer
decoded = sp_tokenizer.decode(generated_ids[0])
print(decoded)
```
# Instruct tokenizer
The HuggingFace tokenizer included in this release should match our own. To compare:
`pip install mistral-common`
```py
from mistral_common.protocol.instruct.messages import (
AssistantMessage,
UserMessage,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
from transformers import AutoTokenizer
tokenizer_v3 = MistralTokenizer.v3()
mistral_query = ChatCompletionRequest(
messages=[
UserMessage(content="How many experts ?"),
AssistantMessage(content="8"),
UserMessage(content="How big ?"),
AssistantMessage(content="22B"),
UserMessage(content="Noice 🎉 !"),
],
model="test",
)
hf_messages = mistral_query.model_dump()['messages']
tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens
tokenizer_hf = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x22B-Instruct-v0.1')
tokenized_hf = tokenizer_hf.apply_chat_template(hf_messages, tokenize=True)
assert tokenized_hf == tokenized_mistral
```
# Function calling and special tokens
This tokenizer includes more special tokens, related to function calling :
- [TOOL_CALLS]
- [AVAILABLE_TOOLS]
- [/AVAILABLE_TOOLS]
- [TOOL_RESULTS]
- [/TOOL_RESULTS]
If you want to use this model with function calling, please be sure to apply it similarly to what is done in our [SentencePieceTokenizerV3](https://github.com/mistralai/mistral-common/blob/main/src/mistral_common/tokens/tokenizers/sentencepiece.py#L299).
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,
Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,
Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,
Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,
Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,
Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,
Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,
Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,
Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,
Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,
Valera Nemychnikova, William El Sayed, William Marshall | {"language": ["en", "es", "it", "de", "fr"], "license": "apache-2.0"} | MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"en",
"es",
"it",
"de",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:47:00+00:00 | [] | [
"en",
"es",
"it",
"de",
"fr"
] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #en #es #it #de #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Mixtral-8x22B-Instruct-v0.1
The Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the Mixtral-8x22B-v0.1.
## Run the model
# Instruct tokenizer
The HuggingFace tokenizer included in this release should match our own. To compare:
'pip install mistral-common'
# Function calling and special tokens
This tokenizer includes more special tokens, related to function calling :
- [TOOL_CALLS]
- [AVAILABLE_TOOLS]
- [/AVAILABLE_TOOLS]
- [TOOL_RESULTS]
- [/TOOL_RESULTS]
If you want to use this model with function calling, please be sure to apply it similarly to what is done in our SentencePieceTokenizerV3.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,
Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,
Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,
Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,
Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,
Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,
Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,
Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,
Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,
Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,
Valera Nemychnikova, William El Sayed, William Marshall | [
"# Model Card for Mixtral-8x22B-Instruct-v0.1\nThe Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the Mixtral-8x22B-v0.1.",
"## Run the model",
"# Instruct tokenizer\nThe HuggingFace tokenizer included in this release should match our own. To compare: \n'pip install mistral-common'",
"# Function calling and special tokens\nThis tokenizer includes more special tokens, related to function calling : \n- [TOOL_CALLS]\n- [AVAILABLE_TOOLS]\n- [/AVAILABLE_TOOLS]\n- [TOOL_RESULTS]\n- [/TOOL_RESULTS]\n\nIf you want to use this model with function calling, please be sure to apply it similarly to what is done in our SentencePieceTokenizerV3.",
"# The Mistral AI Team\nAlbert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,\nArthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,\nBlanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,\nDiego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,\nGianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,\nJean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,\nLucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,\nMarie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,\nPierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,\nThibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,\nValera Nemychnikova, William El Sayed, William Marshall"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #en #es #it #de #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Mixtral-8x22B-Instruct-v0.1\nThe Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the Mixtral-8x22B-v0.1.",
"## Run the model",
"# Instruct tokenizer\nThe HuggingFace tokenizer included in this release should match our own. To compare: \n'pip install mistral-common'",
"# Function calling and special tokens\nThis tokenizer includes more special tokens, related to function calling : \n- [TOOL_CALLS]\n- [AVAILABLE_TOOLS]\n- [/AVAILABLE_TOOLS]\n- [TOOL_RESULTS]\n- [/TOOL_RESULTS]\n\nIf you want to use this model with function calling, please be sure to apply it similarly to what is done in our SentencePieceTokenizerV3.",
"# The Mistral AI Team\nAlbert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,\nArthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,\nBlanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,\nDiego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,\nGianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,\nJean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,\nLucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,\nMarie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,\nPierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,\nThibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,\nValera Nemychnikova, William El Sayed, William Marshall"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/ghbfw2g | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:47:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# timesformer-base-finetuned-k400-kinetic400-subset-epoch6-num_frame_10_myViT2window5_more_data_b4
This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 72
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "base_model": "facebook/timesformer-base-finetuned-k400", "model-index": [{"name": "timesformer-base-finetuned-k400-kinetic400-subset-epoch6-num_frame_10_myViT2window5_more_data_b4", "results": []}]} | JackWong0911/timesformer-base-finetuned-k400-kinetic400-subset-epoch6-num_frame_10_myViT2window5_more_data_b4 | null | [
"transformers",
"tensorboard",
"safetensors",
"timesformer",
"generated_from_trainer",
"base_model:facebook/timesformer-base-finetuned-k400",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:48:02+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #timesformer #generated_from_trainer #base_model-facebook/timesformer-base-finetuned-k400 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# timesformer-base-finetuned-k400-kinetic400-subset-epoch6-num_frame_10_myViT2window5_more_data_b4
This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 72
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 72",
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] | [
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"# timesformer-base-finetuned-k400-kinetic400-subset-epoch6-num_frame_10_myViT2window5_more_data_b4\n\nThis model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 72",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for OrpoLlama3-8B-FT
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card for OrpoLlama3-8B-FT model that has been pushed on the Hub. This model is fine tuned using ORPO.
- **Developed by:** Sonu Kumar
- **Model type:** Llama3
- **License:** Apache 2.0
- **Finetuned from model [optional]:** https://huggingface.co/meta-llama/Meta-Llama-3-8B | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "pipeline_tag": "text-generation"} | skuma307/OrpoLlama3-8B-FT | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:48:26+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #dataset-HuggingFaceH4/ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for OrpoLlama3-8B-FT
## Model Details
### Model Description
This is the model card for OrpoLlama3-8B-FT model that has been pushed on the Hub. This model is fine tuned using ORPO.
- Developed by: Sonu Kumar
- Model type: Llama3
- License: Apache 2.0
- Finetuned from model [optional]: URL | [
"# Model Card for OrpoLlama3-8B-FT",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card for OrpoLlama3-8B-FT model that has been pushed on the Hub. This model is fine tuned using ORPO.\n\n- Developed by: Sonu Kumar\n- Model type: Llama3\n- License: Apache 2.0\n- Finetuned from model [optional]: URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #dataset-HuggingFaceH4/ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for OrpoLlama3-8B-FT",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card for OrpoLlama3-8B-FT model that has been pushed on the Hub. This model is fine tuned using ORPO.\n\n- Developed by: Sonu Kumar\n- Model type: Llama3\n- License: Apache 2.0\n- Finetuned from model [optional]: URL"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/sn6_228 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:48:43+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Direct Use",
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"### Out-of-Scope Use",
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"### Training Data",
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"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"# Model Card for Model ID",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"## Technical Specifications [optional]",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Gpt4_tiny_Seed101 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T12:49:40+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_Gpt4_tiny_Seed101 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
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- load_in_4bit: True
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_compute_dtype: bfloat16
### Framework versions
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | voidful/pythia-160m | null | [
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt_neox #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "test_trainer", "results": []}]} | aboonaji/test_trainer | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:50:01+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test_trainer
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| [
"# test_trainer\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test_trainer\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers |
# mlx-community/Llama-3-Smaug-8B-8bit
This model was converted to MLX format from [`abacusai/Llama-3-Smaug-8B`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/abacusai/Llama-3-Smaug-8B) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3-Smaug-8B-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "llama2", "library_name": "transformers", "tags": ["mlx"]} | mlx-community/Llama-3-Smaug-8B-8bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mlx",
"conversational",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:50:40+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mlx #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mlx-community/Llama-3-Smaug-8B-8bit
This model was converted to MLX format from ['abacusai/Llama-3-Smaug-8B']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/Llama-3-Smaug-8B-8bit\nThis model was converted to MLX format from ['abacusai/Llama-3-Smaug-8B']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mlx #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mlx-community/Llama-3-Smaug-8B-8bit\nThis model was converted to MLX format from ['abacusai/Llama-3-Smaug-8B']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-generation | transformers |
# Gistral 16B (Mistral from 7B to 16B)
We created a model from other cool models to combine everything into one cool model.
**GGUF Version:** [ehristoforu/Gistral-16B-Q4_K_M-GGUF](https://huggingface.co/ehristoforu/Gistral-16B-Q4_K_M-GGUF)
## Model Details
### Model Description
- **Developed by:** [@ehristoforu](https://huggingface.co/ehristoforu)
- **Model type:** Text Generation (conversational)
- **Language(s) (NLP):** English, French, Russian, German, Japanese, Chinese, Korean, Italian, Ukrainian, Code
- **Finetuned from model:** [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## How to Get Started with the Model
Use the code below to get started with the model.
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "ehristoforu/Gistral-16B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## About merge
Base model: mistralai/Mistral-7B-Instruct-v0.2
Merge models:
- Gaivoronsky/Mistral-7B-Saiga
- snorkelai/Snorkel-Mistral-PairRM-DPO
- OpenBuddy/openbuddy-mistral2-7b-v20.3-32k
- meta-math/MetaMath-Mistral-7B
- HuggingFaceH4/mistral-7b-grok
- HuggingFaceH4/mistral-7b-anthropic
- NousResearch/Yarn-Mistral-7b-128k
- ajibawa-2023/Code-Mistral-7B
- SherlockAssistant/Mistral-7B-Instruct-Ukrainian
Merge datasets:
- HuggingFaceH4/grok-conversation-harmless
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized_fixed
- HuggingFaceH4/cai-conversation-harmless
- meta-math/MetaMathQA
- emozilla/yarn-train-tokenized-16k-mistral
- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- teknium/openhermes
- lksy/ru_instruct_gpt4
- IlyaGusev/ru_turbo_saiga
- IlyaGusev/ru_sharegpt_cleaned
- IlyaGusev/oasst1_ru_main_branch | {"language": ["en", "fr", "ru", "de", "ja", "ko", "zh", "it", "uk", "multilingual", "code"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mistral", "gistral", "gistral-16b", "multilingual", "code", "128k", "metamath", "grok-1", "anthropic", "openhermes", "instruct", "merge"], "datasets": ["HuggingFaceH4/grok-conversation-harmless", "HuggingFaceH4/ultrachat_200k", "HuggingFaceH4/ultrafeedback_binarized_fixed", "HuggingFaceH4/cai-conversation-harmless", "meta-math/MetaMathQA", "emozilla/yarn-train-tokenized-16k-mistral", "snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset", "microsoft/orca-math-word-problems-200k", "m-a-p/Code-Feedback", "teknium/openhermes", "lksy/ru_instruct_gpt4", "IlyaGusev/ru_turbo_saiga", "IlyaGusev/ru_sharegpt_cleaned", "IlyaGusev/oasst1_ru_main_branch"], "base_model": ["Gaivoronsky/Mistral-7B-Saiga", "snorkelai/Snorkel-Mistral-PairRM-DPO", "OpenBuddy/openbuddy-mistral2-7b-v20.3-32k", "meta-math/MetaMath-Mistral-7B", "HuggingFaceH4/mistral-7b-grok", "HuggingFaceH4/mistral-7b-anthropic", "NousResearch/Yarn-Mistral-7b-128k", "ajibawa-2023/Code-Mistral-7B", "SherlockAssistant/Mistral-7B-Instruct-Ukrainian"], "pipeline_tag": "text-generation"} | ehristoforu/Gistral-16B | null | [
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"safetensors",
"mistral",
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"gistral-16b",
"multilingual",
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"dataset:meta-math/MetaMathQA",
"dataset:emozilla/yarn-train-tokenized-16k-mistral",
"dataset:snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/Code-Feedback",
"dataset:teknium/openhermes",
"dataset:lksy/ru_instruct_gpt4",
"dataset:IlyaGusev/ru_turbo_saiga",
"dataset:IlyaGusev/ru_sharegpt_cleaned",
"dataset:IlyaGusev/oasst1_ru_main_branch",
"base_model:Gaivoronsky/Mistral-7B-Saiga",
"base_model:snorkelai/Snorkel-Mistral-PairRM-DPO",
"base_model:OpenBuddy/openbuddy-mistral2-7b-v20.3-32k",
"base_model:meta-math/MetaMath-Mistral-7B",
"base_model:HuggingFaceH4/mistral-7b-grok",
"base_model:HuggingFaceH4/mistral-7b-anthropic",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:ajibawa-2023/Code-Mistral-7B",
"base_model:SherlockAssistant/Mistral-7B-Instruct-Ukrainian",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:51:47+00:00 | [] | [
"en",
"fr",
"ru",
"de",
"ja",
"ko",
"zh",
"it",
"uk",
"multilingual",
"code"
] | TAGS
#transformers #safetensors #mistral #text-generation #gistral #gistral-16b #multilingual #code #128k #metamath #grok-1 #anthropic #openhermes #instruct #merge #en #fr #ru #de #ja #ko #zh #it #uk #dataset-HuggingFaceH4/grok-conversation-harmless #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized_fixed #dataset-HuggingFaceH4/cai-conversation-harmless #dataset-meta-math/MetaMathQA #dataset-emozilla/yarn-train-tokenized-16k-mistral #dataset-snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-teknium/openhermes #dataset-lksy/ru_instruct_gpt4 #dataset-IlyaGusev/ru_turbo_saiga #dataset-IlyaGusev/ru_sharegpt_cleaned #dataset-IlyaGusev/oasst1_ru_main_branch #base_model-Gaivoronsky/Mistral-7B-Saiga #base_model-snorkelai/Snorkel-Mistral-PairRM-DPO #base_model-OpenBuddy/openbuddy-mistral2-7b-v20.3-32k #base_model-meta-math/MetaMath-Mistral-7B #base_model-HuggingFaceH4/mistral-7b-grok #base_model-HuggingFaceH4/mistral-7b-anthropic #base_model-NousResearch/Yarn-Mistral-7b-128k #base_model-ajibawa-2023/Code-Mistral-7B #base_model-SherlockAssistant/Mistral-7B-Instruct-Ukrainian #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Gistral 16B (Mistral from 7B to 16B)
!logo
We created a model from other cool models to combine everything into one cool model.
GGUF Version: ehristoforu/Gistral-16B-Q4_K_M-GGUF
## Model Details
### Model Description
- Developed by: @ehristoforu
- Model type: Text Generation (conversational)
- Language(s) (NLP): English, French, Russian, German, Japanese, Chinese, Korean, Italian, Ukrainian, Code
- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2
## How to Get Started with the Model
Use the code below to get started with the model.
## About merge
Base model: mistralai/Mistral-7B-Instruct-v0.2
Merge models:
- Gaivoronsky/Mistral-7B-Saiga
- snorkelai/Snorkel-Mistral-PairRM-DPO
- OpenBuddy/openbuddy-mistral2-7b-v20.3-32k
- meta-math/MetaMath-Mistral-7B
- HuggingFaceH4/mistral-7b-grok
- HuggingFaceH4/mistral-7b-anthropic
- NousResearch/Yarn-Mistral-7b-128k
- ajibawa-2023/Code-Mistral-7B
- SherlockAssistant/Mistral-7B-Instruct-Ukrainian
Merge datasets:
- HuggingFaceH4/grok-conversation-harmless
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized_fixed
- HuggingFaceH4/cai-conversation-harmless
- meta-math/MetaMathQA
- emozilla/yarn-train-tokenized-16k-mistral
- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- teknium/openhermes
- lksy/ru_instruct_gpt4
- IlyaGusev/ru_turbo_saiga
- IlyaGusev/ru_sharegpt_cleaned
- IlyaGusev/oasst1_ru_main_branch | [
"# Gistral 16B (Mistral from 7B to 16B)\n\n!logo\n\nWe created a model from other cool models to combine everything into one cool model.\n\nGGUF Version: ehristoforu/Gistral-16B-Q4_K_M-GGUF",
"## Model Details",
"### Model Description \n\n- Developed by: @ehristoforu\n- Model type: Text Generation (conversational)\n- Language(s) (NLP): English, French, Russian, German, Japanese, Chinese, Korean, Italian, Ukrainian, Code\n- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## About merge\n\nBase model: mistralai/Mistral-7B-Instruct-v0.2\n\nMerge models:\n- Gaivoronsky/Mistral-7B-Saiga\n- snorkelai/Snorkel-Mistral-PairRM-DPO\n- OpenBuddy/openbuddy-mistral2-7b-v20.3-32k\n- meta-math/MetaMath-Mistral-7B\n- HuggingFaceH4/mistral-7b-grok\n- HuggingFaceH4/mistral-7b-anthropic\n- NousResearch/Yarn-Mistral-7b-128k\n- ajibawa-2023/Code-Mistral-7B\n- SherlockAssistant/Mistral-7B-Instruct-Ukrainian\n\nMerge datasets:\n- HuggingFaceH4/grok-conversation-harmless\n- HuggingFaceH4/ultrachat_200k\n- HuggingFaceH4/ultrafeedback_binarized_fixed\n- HuggingFaceH4/cai-conversation-harmless\n- meta-math/MetaMathQA\n- emozilla/yarn-train-tokenized-16k-mistral\n- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset\n- microsoft/orca-math-word-problems-200k\n- m-a-p/Code-Feedback\n- teknium/openhermes\n- lksy/ru_instruct_gpt4\n- IlyaGusev/ru_turbo_saiga\n- IlyaGusev/ru_sharegpt_cleaned\n- IlyaGusev/oasst1_ru_main_branch"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #gistral #gistral-16b #multilingual #code #128k #metamath #grok-1 #anthropic #openhermes #instruct #merge #en #fr #ru #de #ja #ko #zh #it #uk #dataset-HuggingFaceH4/grok-conversation-harmless #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized_fixed #dataset-HuggingFaceH4/cai-conversation-harmless #dataset-meta-math/MetaMathQA #dataset-emozilla/yarn-train-tokenized-16k-mistral #dataset-snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-teknium/openhermes #dataset-lksy/ru_instruct_gpt4 #dataset-IlyaGusev/ru_turbo_saiga #dataset-IlyaGusev/ru_sharegpt_cleaned #dataset-IlyaGusev/oasst1_ru_main_branch #base_model-Gaivoronsky/Mistral-7B-Saiga #base_model-snorkelai/Snorkel-Mistral-PairRM-DPO #base_model-OpenBuddy/openbuddy-mistral2-7b-v20.3-32k #base_model-meta-math/MetaMath-Mistral-7B #base_model-HuggingFaceH4/mistral-7b-grok #base_model-HuggingFaceH4/mistral-7b-anthropic #base_model-NousResearch/Yarn-Mistral-7b-128k #base_model-ajibawa-2023/Code-Mistral-7B #base_model-SherlockAssistant/Mistral-7B-Instruct-Ukrainian #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Gistral 16B (Mistral from 7B to 16B)\n\n!logo\n\nWe created a model from other cool models to combine everything into one cool model.\n\nGGUF Version: ehristoforu/Gistral-16B-Q4_K_M-GGUF",
"## Model Details",
"### Model Description \n\n- Developed by: @ehristoforu\n- Model type: Text Generation (conversational)\n- Language(s) (NLP): English, French, Russian, German, Japanese, Chinese, Korean, Italian, Ukrainian, Code\n- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## About merge\n\nBase model: mistralai/Mistral-7B-Instruct-v0.2\n\nMerge models:\n- Gaivoronsky/Mistral-7B-Saiga\n- snorkelai/Snorkel-Mistral-PairRM-DPO\n- OpenBuddy/openbuddy-mistral2-7b-v20.3-32k\n- meta-math/MetaMath-Mistral-7B\n- HuggingFaceH4/mistral-7b-grok\n- HuggingFaceH4/mistral-7b-anthropic\n- NousResearch/Yarn-Mistral-7b-128k\n- ajibawa-2023/Code-Mistral-7B\n- SherlockAssistant/Mistral-7B-Instruct-Ukrainian\n\nMerge datasets:\n- HuggingFaceH4/grok-conversation-harmless\n- HuggingFaceH4/ultrachat_200k\n- HuggingFaceH4/ultrafeedback_binarized_fixed\n- HuggingFaceH4/cai-conversation-harmless\n- meta-math/MetaMathQA\n- emozilla/yarn-train-tokenized-16k-mistral\n- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset\n- microsoft/orca-math-word-problems-200k\n- m-a-p/Code-Feedback\n- teknium/openhermes\n- lksy/ru_instruct_gpt4\n- IlyaGusev/ru_turbo_saiga\n- IlyaGusev/ru_sharegpt_cleaned\n- IlyaGusev/oasst1_ru_main_branch"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-roberta-base-sentiment-latest-trump-stance-3
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2191
- Accuracy: {'accuracy': 0.92375}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:-----------------------:|
| 0.4724 | 1.0 | 2560 | 0.3901 | {'accuracy': 0.833125} |
| 0.4851 | 2.0 | 5120 | 0.3255 | {'accuracy': 0.8765625} |
| 0.4811 | 3.0 | 7680 | 0.3562 | {'accuracy': 0.868125} |
| 0.4828 | 4.0 | 10240 | 0.6211 | {'accuracy': 0.8375} |
| 0.4136 | 5.0 | 12800 | 0.4535 | {'accuracy': 0.8496875} |
| 0.4509 | 6.0 | 15360 | 0.2934 | {'accuracy': 0.8915625} |
| 0.4426 | 7.0 | 17920 | 0.3903 | {'accuracy': 0.8775} |
| 0.4393 | 8.0 | 20480 | 0.2773 | {'accuracy': 0.890625} |
| 0.4314 | 9.0 | 23040 | 0.2834 | {'accuracy': 0.895625} |
| 0.4222 | 10.0 | 25600 | 0.3127 | {'accuracy': 0.893125} |
| 0.396 | 11.0 | 28160 | 0.2732 | {'accuracy': 0.8984375} |
| 0.4185 | 12.0 | 30720 | 0.2628 | {'accuracy': 0.9034375} |
| 0.4068 | 13.0 | 33280 | 0.2689 | {'accuracy': 0.9053125} |
| 0.432 | 14.0 | 35840 | 0.3075 | {'accuracy': 0.88375} |
| 0.4292 | 15.0 | 38400 | 0.3339 | {'accuracy': 0.8878125} |
| 0.4468 | 16.0 | 40960 | 0.2573 | {'accuracy': 0.8971875} |
| 0.438 | 17.0 | 43520 | 0.2787 | {'accuracy': 0.9028125} |
| 0.4164 | 18.0 | 46080 | 0.2621 | {'accuracy': 0.905625} |
| 0.3823 | 19.0 | 48640 | 0.2272 | {'accuracy': 0.9146875} |
| 0.3487 | 20.0 | 51200 | 0.2917 | {'accuracy': 0.8996875} |
| 0.4165 | 21.0 | 53760 | 0.2238 | {'accuracy': 0.9184375} |
| 0.3776 | 22.0 | 56320 | 0.2620 | {'accuracy': 0.908125} |
| 0.4304 | 23.0 | 58880 | 0.2383 | {'accuracy': 0.908125} |
| 0.4152 | 24.0 | 61440 | 0.3826 | {'accuracy': 0.8746875} |
| 0.4024 | 25.0 | 64000 | 0.2482 | {'accuracy': 0.9121875} |
| 0.3547 | 26.0 | 66560 | 0.4049 | {'accuracy': 0.8778125} |
| 0.3632 | 27.0 | 69120 | 0.2304 | {'accuracy': 0.916875} |
| 0.3732 | 28.0 | 71680 | 0.4476 | {'accuracy': 0.8721875} |
| 0.3438 | 29.0 | 74240 | 0.2521 | {'accuracy': 0.9090625} |
| 0.3871 | 30.0 | 76800 | 0.3117 | {'accuracy': 0.894375} |
| 0.3654 | 31.0 | 79360 | 0.2647 | {'accuracy': 0.908125} |
| 0.3838 | 32.0 | 81920 | 0.2181 | {'accuracy': 0.9184375} |
| 0.3657 | 33.0 | 84480 | 0.2106 | {'accuracy': 0.9228125} |
| 0.357 | 34.0 | 87040 | 0.2231 | {'accuracy': 0.923125} |
| 0.3807 | 35.0 | 89600 | 0.2420 | {'accuracy': 0.9121875} |
| 0.3374 | 36.0 | 92160 | 0.2927 | {'accuracy': 0.895625} |
| 0.304 | 37.0 | 94720 | 0.2226 | {'accuracy': 0.9203125} |
| 0.3322 | 38.0 | 97280 | 0.2471 | {'accuracy': 0.9140625} |
| 0.3522 | 39.0 | 99840 | 0.2443 | {'accuracy': 0.9134375} |
| 0.3124 | 40.0 | 102400 | 0.2410 | {'accuracy': 0.91625} |
| 0.3095 | 41.0 | 104960 | 0.2237 | {'accuracy': 0.91875} |
| 0.3142 | 42.0 | 107520 | 0.2396 | {'accuracy': 0.918125} |
| 0.3203 | 43.0 | 110080 | 0.2191 | {'accuracy': 0.916875} |
| 0.2913 | 44.0 | 112640 | 0.2298 | {'accuracy': 0.9221875} |
| 0.3308 | 45.0 | 115200 | 0.2188 | {'accuracy': 0.924375} |
| 0.2614 | 46.0 | 117760 | 0.2437 | {'accuracy': 0.914375} |
| 0.2903 | 47.0 | 120320 | 0.2507 | {'accuracy': 0.911875} |
| 0.3421 | 48.0 | 122880 | 0.2119 | {'accuracy': 0.9246875} |
| 0.3158 | 49.0 | 125440 | 0.2117 | {'accuracy': 0.9240625} |
| 0.326 | 50.0 | 128000 | 0.2191 | {'accuracy': 0.92375} |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "model-index": [{"name": "twitter-roberta-base-sentiment-latest-trump-stance-3", "results": []}]} | saideep-arikontham/twitter-roberta-base-sentiment-latest-trump-stance-3 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"region:us"
] | null | 2024-04-21T12:52:06+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-cardiffnlp/twitter-roberta-base-sentiment-latest #region-us
| twitter-roberta-base-sentiment-latest-trump-stance-3
====================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2191
* Accuracy: {'accuracy': 0.92375}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 50
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.38.2
* Pytorch 2.2.1
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.2.1\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 5\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.2.1\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/s6_246 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T12:53:32+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
object-detection | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6853 | 0.32 | 50 | 1.8717 |
| 1.6419 | 0.64 | 100 | 1.4752 |
| 1.4029 | 0.96 | 150 | 1.2728 |
| 1.2596 | 1.27 | 200 | 1.1683 |
| 1.1761 | 1.59 | 250 | 1.0847 |
| 1.106 | 1.91 | 300 | 1.0396 |
| 1.0586 | 2.23 | 350 | 0.9886 |
| 1.0258 | 2.55 | 400 | 0.9653 |
| 1.0151 | 2.87 | 450 | 0.9448 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | jbossenk/detr | null | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T12:59:56+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
| detr
====
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9448
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
null | null |
# T3qCalmexperiment-7B
T3qCalmexperiment-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
- model: allknowingroger/CalmExperiment-7B-slerp
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/T3qCalmexperiment-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/T3qCalmexperiment-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T13:00:22+00:00 | [] | [] | TAGS
#merge #mergekit #lazymergekit #automerger #license-apache-2.0 #region-us
|
# T3qCalmexperiment-7B
T3qCalmexperiment-7B is an automated merge created by Maxime Labonne using the following configuration.
## Configuration
## Usage
| [
"# T3qCalmexperiment-7B\n\nT3qCalmexperiment-7B is an automated merge created by Maxime Labonne using the following configuration.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#merge #mergekit #lazymergekit #automerger #license-apache-2.0 #region-us \n",
"# T3qCalmexperiment-7B\n\nT3qCalmexperiment-7B is an automated merge created by Maxime Labonne using the following configuration.",
"## Configuration",
"## Usage"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** junelegend
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
## Model Details
This model is finetuned on [adeocybersecurity/DockerCommand](https://huggingface.co/datasets/adeocybersecurity/DockerCommand) dataset using the base [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) model. These are only the lora adapaters of the model, the base model is automatically downloaded.
## How to use
```
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "llama-3-docker-command-lora",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"translate this sentence in docker command.", # instruction
"Give me a list of all containers, indicating their status as well.", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)
```
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "datasets": ["adeocybersecurity/DockerCommand"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "pipeline_tag": "text-generation"} | junelegend/llama-3-docker-command-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"text-generation",
"en",
"dataset:adeocybersecurity/DockerCommand",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:00:57+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #text-generation #en #dataset-adeocybersecurity/DockerCommand #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: junelegend
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
## Model Details
This model is finetuned on adeocybersecurity/DockerCommand dataset using the base unsloth/llama-3-8b-bnb-4bit model. These are only the lora adapaters of the model, the base model is automatically downloaded.
## How to use
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/> | [
"# Uploaded model\n\n- Developed by: junelegend\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit",
"## Model Details\n\nThis model is finetuned on adeocybersecurity/DockerCommand dataset using the base unsloth/llama-3-8b-bnb-4bit model. These are only the lora adapaters of the model, the base model is automatically downloaded.",
"## How to use\n\n\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #text-generation #en #dataset-adeocybersecurity/DockerCommand #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: junelegend\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit",
"## Model Details\n\nThis model is finetuned on adeocybersecurity/DockerCommand dataset using the base unsloth/llama-3-8b-bnb-4bit model. These are only the lora adapaters of the model, the base model is automatically downloaded.",
"## How to use\n\n\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Imcf1Y3FSatM/weblab-10b-neo-len4096-1500iteration-base | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:01:11+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"### Training Data",
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"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
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"### Compute Infrastructure",
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"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model
Model Version: 1.0
Model Creator: CollAIborator (https://www.collaiborate.com)
Model Overview: This repo contains 4 Bit quantized GPTQ model files from mistralai/Mistral-7B-Instruct-v0.2. This model is an optimized version to run on lower config GPUs and comes with a small quality degradation from the original model but the intent was to make Mistral-7B-Instruct-v0.2 available for use in smaller GPUs with maximum improvement in latency and throughput.
Intended Use: The GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model is intended to be used for tasks involving instructional text comprehension, such as question answering, summarization, and instructional text generation. It can be deployed in applications where understanding and generating instructional content is crucial, including educational platforms, virtual assistants, and content recommendation systems.
Limitations and Considerations: While the GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model demonstrates strong performance in tasks related to instructional text comprehension, it may not perform optimally in domains or tasks outside its training data distribution. Users should evaluate the model's performance on specific tasks and datasets before deploying it in production environments.
Ethical Considerations: As with any language model, the GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model can potentially generate biased or inappropriate content based on the input it receives. Users are encouraged to monitor and evaluate the model's outputs to ensure they align with ethical guidelines and do not propagate harmful stereotypes or misinformation.
Disclaimer: The GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model is provided by CollAIborator and is offered as-is, without any warranty or guarantee of performance. Users are solely responsible for the use and outcomes of the model in their applications.
Developed by: CollAIborator team
Model type: Text Generation
Language(s) (NLP): en
License: apache-2.0
Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "quantized", "finetuned", "gptq", "mistral"]} | SrikanthChellappa/Mistral-7B-Instruct-v0.2-GPTQ-4Bit | null | [
"transformers",
"mistral",
"text-generation",
"text-generation-inference",
"quantized",
"finetuned",
"gptq",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-04-21T13:02:28+00:00 | [] | [
"en"
] | TAGS
#transformers #mistral #text-generation #text-generation-inference #quantized #finetuned #gptq #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model
Model Version: 1.0
Model Creator: CollAIborator (URL)
Model Overview: This repo contains 4 Bit quantized GPTQ model files from mistralai/Mistral-7B-Instruct-v0.2. This model is an optimized version to run on lower config GPUs and comes with a small quality degradation from the original model but the intent was to make Mistral-7B-Instruct-v0.2 available for use in smaller GPUs with maximum improvement in latency and throughput.
Intended Use: The GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model is intended to be used for tasks involving instructional text comprehension, such as question answering, summarization, and instructional text generation. It can be deployed in applications where understanding and generating instructional content is crucial, including educational platforms, virtual assistants, and content recommendation systems.
Limitations and Considerations: While the GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model demonstrates strong performance in tasks related to instructional text comprehension, it may not perform optimally in domains or tasks outside its training data distribution. Users should evaluate the model's performance on specific tasks and datasets before deploying it in production environments.
Ethical Considerations: As with any language model, the GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model can potentially generate biased or inappropriate content based on the input it receives. Users are encouraged to monitor and evaluate the model's outputs to ensure they align with ethical guidelines and do not propagate harmful stereotypes or misinformation.
Disclaimer: The GPTQ 4-bit Quantized Mistral-7B-Instruct-v0.2 Model is provided by CollAIborator and is offered as-is, without any warranty or guarantee of performance. Users are solely responsible for the use and outcomes of the model in their applications.
Developed by: CollAIborator team
Model type: Text Generation
Language(s) (NLP): en
License: apache-2.0
Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 | [] | [
"TAGS\n#transformers #mistral #text-generation #text-generation-inference #quantized #finetuned #gptq #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us \n"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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<!-- This should link to a Dataset Card if possible. -->
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | domenicrosati/lens-loss-minimality_lr_2e-5_attack_meta-llama_Llama-2-7b-chat-hf_1_num_layers_6_6e-5_1k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:02:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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- Finetuned from model [optional]:
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- Paper [optional]:
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## Uses
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### Out-of-Scope Use
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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- Hardware Type:
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- Cloud Provider:
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## Technical Specifications [optional]
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#### Software
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## Glossary [optional]
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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] |
text-to-image | diffusers | <html>
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<span>Adeptus Custodes</span>
</h1>
</div>
<figure>
<img src="assest/sample-image.png" alt="sample image" class="center"/>
<figcaption>Generated with Epoch 10</figcaption>
</figure>
</html>
<!-- START Model Info -->
<div class="model-info-container" style='font-family:Monospace'>
<table id="model-info-table">
<tr>
<th></th>
<th></th>
</tr>
<tr>
<td>Base Model</td>
<td>SDXL v1.0</td>
</tr>
<tr>
<td>VAE</td>
<td><a href="https://huggingface.co/madebyollin/sdxl-vae-fp16-fix">SDXL-VAE-FP16-FIX</a></td>
</tr>
<tr>
<td>Type</td>
<td>LoRA</td>
</tr>
</table>
</div>
<!-- END Model Info --> | {"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion", "stable-diffusion-xl", "SDXL", "LoRA"]} | nDimensional/Custodes-SDXL-LoRA-Warhammer-40k | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"SDXL",
"LoRA",
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-21T13:02:59+00:00 | [] | [
"en"
] | TAGS
#diffusers #text-to-image #stable-diffusion #stable-diffusion-xl #SDXL #LoRA #en #license-creativeml-openrail-m #region-us
| <html>
<head>
<link href="URL rel="stylesheet">
</head>
<style>
.title-container {
display: flex;
justify-content: center;
align-items: center;
}
.title {
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text-align: center;
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<span>Adeptus Custodes</span>
</h1>
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<figure>
<img src="assest/URL" alt="sample image" class="center"/>
<figcaption>Generated with Epoch 10</figcaption>
</figure>
</html>
<div class="model-info-container" style='font-family:Monospace'>
<table id="model-info-table">
<tr>
<th></th>
<th></th>
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<tr>
<td>Base Model</td>
<td>SDXL v1.0</td>
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<tr>
<td>VAE</td>
<td><a href="URL
</tr>
<tr>
<td>Type</td>
<td>LoRA</td>
</tr>
</table>
</div>
| [] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #stable-diffusion-xl #SDXL #LoRA #en #license-creativeml-openrail-m #region-us \n"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-toxic2nontoxic-900 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:03:19+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
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"### Model Architecture and Objective",
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"## Model Card Contact"
] |
text-generation | transformers |
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. 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The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"} | ByteResearch/Llama-3-8B-Instruct | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:08:17+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Details
-------------
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
Model developers Meta
Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
Input Models input text only.
Output Models generate text and code only.
Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
Model Release Date April 18, 2024.
Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
License A custom commercial license is available at: URL
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go here.
Intended Use
------------
Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English.
Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
How to use
----------
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original 'llama3' codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.
#### Transformers pipeline
#### Transformers AutoModelForCausalLM
### Use with 'llama3'
Please, follow the instructions in the repository
To download Original checkpoints, see the example command below leveraging 'huggingface-cli':
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
Hardware and Software
---------------------
Training Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
Training Data
-------------
Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
Benchmarks
----------
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.
### Base pretrained models
### Instruction tuned models
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
Safety
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
Refusals
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL
#### Critical risks
CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### Cyber Security
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.
### Child Safety
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.
Finally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.
Ethical Considerations and Limitations
--------------------------------------
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at URL
instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {URL
}
Contributors
------------
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| [
"### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.",
"#### Transformers pipeline",
"#### Transformers AutoModelForCausalLM",
"### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.",
"### Base pretrained models",
"### Instruction tuned models",
"### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.",
"#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.",
"#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL",
"#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).",
"### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.",
"### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.",
"### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.",
"#### Transformers pipeline",
"#### Transformers AutoModelForCausalLM",
"### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.",
"### Base pretrained models",
"### Instruction tuned models",
"### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.",
"#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.",
"#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL",
"#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).",
"### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.",
"### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.",
"### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos"
] |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | HuggingFaceFW/ablation-model-slimpajama | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:12:06+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | HuggingFaceFW/ablation-model-the-pile | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:12:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Yi-6B-ruozhiba4
This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the ruozhiba dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1871 | 1.0 | 20 | 2.1112 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["ruozhiba"], "base_model": "01-ai/Yi-6B", "model-index": [{"name": "Yi-6B-ruozhiba4", "results": []}]} | yyx123/Yi-6B-ruozhiba4 | null | [
"peft",
"safetensors",
"llama",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:ruozhiba",
"base_model:01-ai/Yi-6B",
"license:other",
"4-bit",
"region:us"
] | null | 2024-04-21T13:12:51+00:00 | [] | [] | TAGS
#peft #safetensors #llama #alignment-handbook #generated_from_trainer #trl #sft #dataset-ruozhiba #base_model-01-ai/Yi-6B #license-other #4-bit #region-us
| Yi-6B-ruozhiba4
===============
This model is a fine-tuned version of 01-ai/Yi-6B on the ruozhiba dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1112
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1
### Training results
### Framework versions
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.2.2+cu118
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #llama #alignment-handbook #generated_from_trainer #trl #sft #dataset-ruozhiba #base_model-01-ai/Yi-6B #license-other #4-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Mistral-offspring-1-3
Mistral-offspring-1-3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [akjindal53244/Arithmo-Mistral-7B](https://huggingface.co/akjindal53244/Arithmo-Mistral-7B)
* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: akjindal53244/Arithmo-Mistral-7B
parameters:
density: 0.5
weight: 0.5
- model: meta-math/MetaMath-Mistral-7B
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "PotatoB/Mistral-offspring-1-3"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "akjindal53244/Arithmo-Mistral-7B", "meta-math/MetaMath-Mistral-7B"], "base_model": ["akjindal53244/Arithmo-Mistral-7B", "meta-math/MetaMath-Mistral-7B"]} | PotatoB/Mistral-offspring-1-3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"akjindal53244/Arithmo-Mistral-7B",
"meta-math/MetaMath-Mistral-7B",
"base_model:akjindal53244/Arithmo-Mistral-7B",
"base_model:meta-math/MetaMath-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:13:04+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #akjindal53244/Arithmo-Mistral-7B #meta-math/MetaMath-Mistral-7B #base_model-akjindal53244/Arithmo-Mistral-7B #base_model-meta-math/MetaMath-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Mistral-offspring-1-3
Mistral-offspring-1-3 is a merge of the following models using LazyMergekit:
* akjindal53244/Arithmo-Mistral-7B
* meta-math/MetaMath-Mistral-7B
## Configuration
## Usage
| [
"# Mistral-offspring-1-3\n\nMistral-offspring-1-3 is a merge of the following models using LazyMergekit:\n* akjindal53244/Arithmo-Mistral-7B\n* meta-math/MetaMath-Mistral-7B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #akjindal53244/Arithmo-Mistral-7B #meta-math/MetaMath-Mistral-7B #base_model-akjindal53244/Arithmo-Mistral-7B #base_model-meta-math/MetaMath-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mistral-offspring-1-3\n\nMistral-offspring-1-3 is a merge of the following models using LazyMergekit:\n* akjindal53244/Arithmo-Mistral-7B\n* meta-math/MetaMath-Mistral-7B",
"## Configuration",
"## Usage"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | domenicrosati/adversarial_loss_lr_1e-5_attack_meta-llama_Llama-2-7b-chat-hf_masked_4_6e-5_1k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:14:03+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
"#### Factors",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7861
- Accuracy: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9073 | 1.0 | 113 | 1.8699 | 0.4 |
| 1.3144 | 2.0 | 226 | 1.2309 | 0.625 |
| 0.8747 | 3.0 | 339 | 0.9318 | 0.74 |
| 0.6776 | 4.0 | 452 | 0.8368 | 0.735 |
| 0.383 | 5.0 | 565 | 0.6930 | 0.745 |
| 0.3383 | 6.0 | 678 | 0.8012 | 0.755 |
| 0.2922 | 7.0 | 791 | 0.6724 | 0.78 |
| 0.1086 | 8.0 | 904 | 0.7984 | 0.755 |
| 0.0409 | 9.0 | 1017 | 0.7385 | 0.805 |
| 0.0507 | 10.0 | 1130 | 0.6669 | 0.805 |
| 0.0424 | 11.0 | 1243 | 0.7698 | 0.815 |
| 0.0078 | 12.0 | 1356 | 0.7985 | 0.81 |
| 0.0068 | 13.0 | 1469 | 0.7679 | 0.81 |
| 0.0063 | 14.0 | 1582 | 0.8139 | 0.795 |
| 0.0065 | 15.0 | 1695 | 0.7861 | 0.8 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan"}, "metrics": [{"type": "accuracy", "value": 0.8, "name": "Accuracy"}]}]}]} | saketag73/classification_distilhubert-finetuned-gtzan-5 | null | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:14:32+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us
| distilhubert-finetuned-gtzan
============================
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7861
* Accuracy: 0.8
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | This is an ExLlamaV2 quantized model in 4bpw of [Muhammad2003/Llama3-8B-OpenHermes-DPO](https://huggingface.co/Muhammad2003/Llama3-8B-OpenHermes-DPO) using the default calibration dataset.
# Original Model card:
# Llama3-8B-OpenHermes-DPO
Llama3-8B-OpenHermes-DPO is DPO-Finetuned model of Llama3-8B, on the OpenHermes-2.5 preference dataset using QLoRA.
* [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)
</details><br>
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Muhammad2003/Llama3-8B-OpenHermes-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 🏆 Evaluation results
### Coming Soon | {"license": "apache-2.0", "library_name": "transformers", "tags": ["DPO", "Llama3-8B"], "datasets": "mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha"} | mpasila/Llama3-8B-OpenHermes-DPO-exl2-4bpw | null | [
"transformers",
"llama",
"text-generation",
"DPO",
"Llama3-8B",
"conversational",
"dataset:mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:16:30+00:00 | [] | [] | TAGS
#transformers #llama #text-generation #DPO #Llama3-8B #conversational #dataset-mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is an ExLlamaV2 quantized model in 4bpw of Muhammad2003/Llama3-8B-OpenHermes-DPO using the default calibration dataset.
# Original Model card:
# Llama3-8B-OpenHermes-DPO
!image/png
Llama3-8B-OpenHermes-DPO is DPO-Finetuned model of Llama3-8B, on the OpenHermes-2.5 preference dataset using QLoRA.
* meta-llama/Meta-Llama-3-8B
* mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
</details><br>
## Usage
## Evaluation results
### Coming Soon | [
"# Original Model card:",
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"## Usage",
"## Evaluation results",
"### Coming Soon"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** Alsebay
- **License:** apache-2.0
- **Finetuned from model :** Sao10K/Fimbulvetr-11B-v2
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "Sao10K/Fimbulvetr-11B-v2"} | Alsebay/test-model-v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:Sao10K/Fimbulvetr-11B-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:17:28+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-Sao10K/Fimbulvetr-11B-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Alsebay
- License: apache-2.0
- Finetuned from model : Sao10K/Fimbulvetr-11B-v2
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
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] | [
"TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-Sao10K/Fimbulvetr-11B-v2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Alsebay\n- License: apache-2.0\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
null | transformers |
# Uploaded model
- **Developed by:** ogdanneedham
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | ogdanneedham/mistral-gs-0.3-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:21:16+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: ogdanneedham
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-476.77 +/- 105.24", "name": "mean_reward", "verified": false}]}]}]} | tomTs/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-21T13:22:02+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | isemmanuelolowe/Jamba9B | null | [
"transformers",
"safetensors",
"jamba",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:23:41+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #jamba #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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| [
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Tobistd/test-gpt4 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:30:01+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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- Hardware Type:
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### Compute Infrastructure
#### Hardware
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
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"## Training Details",
"### Training Data",
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] |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speaker-segmentation-fine-tuned-callhome-spa
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome spa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5198
- Der: 0.1745
- False Alarm: 0.0739
- Missed Detection: 0.0687
- Confusion: 0.0319
## Model description
This segmentation model has been trained on Spanish data (Callhome) using [diarizers](https://github.com/huggingface/diarizers/tree/main).
It can be loaded with two lines of code:
```python
from diarizers import SegmentationModel
segmentation_model = SegmentationModel().from_pretrained('diarizers-community/speaker-segmentation-fine-tuned-callhome-spa')
```
To use it within a pyannote speaker diarization pipeline, load the [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) pipeline, and convert the model to a pyannote compatible format:
```python
from pyannote.audio import Pipeline
import torch
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
# load the pre-trained pyannote pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
pipeline.to(device)
# replace the segmentation model with your fine-tuned one
segmentation_model = segmentation_model.to_pyannote_model()
pipeline._segmentation.model = segmentation_model.to(device)
```
You can now use the pipeline on audio examples:
```python
from datasets import load_dataset
# load dataset example
dataset = load_dataset("diarizers-community/callhome", "spa", split="data")
sample = dataset[0]["audio"]
# pre-process inputs
sample["waveform"] = torch.from_numpy(sample.pop("array")[None, :]).to(device, dtype=model.dtype)
sample["sample_rate"] = sample.pop("sampling_rate")
# perform inference
diarization = pipeline(sample)
# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.655 | 1.0 | 382 | 0.5330 | 0.1799 | 0.0680 | 0.0756 | 0.0364 |
| 0.6293 | 2.0 | 764 | 0.5216 | 0.1747 | 0.0662 | 0.0746 | 0.0339 |
| 0.6145 | 3.0 | 1146 | 0.5244 | 0.1770 | 0.0759 | 0.0686 | 0.0325 |
| 0.5956 | 4.0 | 1528 | 0.5185 | 0.1734 | 0.0732 | 0.0687 | 0.0315 |
| 0.5989 | 5.0 | 1910 | 0.5198 | 0.1745 | 0.0739 | 0.0687 | 0.0319 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["speaker-diarization", "speaker-segmentation", "generated_from_trainer"], "datasets": ["diarizers-community/callhome"], "base_model": "pyannote/segmentation-3.0", "model-index": [{"name": "speaker-segmentation-fine-tuned-callhome-spa", "results": []}]} | diarizers-community/speaker-segmentation-fine-tuned-callhome-spa | null | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:diarizers-community/callhome",
"base_model:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T13:30:39+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #pyannet #speaker-diarization #speaker-segmentation #generated_from_trainer #dataset-diarizers-community/callhome #base_model-pyannote/segmentation-3.0 #license-mit #endpoints_compatible #region-us
| speaker-segmentation-fine-tuned-callhome-spa
============================================
This model is a fine-tuned version of pyannote/segmentation-3.0 on the diarizers-community/callhome spa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5198
* Der: 0.1745
* False Alarm: 0.0739
* Missed Detection: 0.0687
* Confusion: 0.0319
Model description
-----------------
This segmentation model has been trained on Spanish data (Callhome) using diarizers.
It can be loaded with two lines of code:
To use it within a pyannote speaker diarization pipeline, load the pyannote/speaker-diarization-3.1 pipeline, and convert the model to a pyannote compatible format:
You can now use the pipeline on audio examples:
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #pyannet #speaker-diarization #speaker-segmentation #generated_from_trainer #dataset-diarizers-community/callhome #base_model-pyannote/segmentation-3.0 #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is based on the [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on most benchmarks (see results).
It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
## Quantized models
* **GGUF**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
* **AWQ**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
* **GPTQ**: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ
* **EXL2**:
* 3.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-3.0bpw-h6-exl2
* 4.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-4.0bpw-h6-exl2
* 5.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-5.0bpw-h6-exl2
* 6.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-6.0bpw-h6-exl2
* 8.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-8.0bpw-h8-exl2
## Results
**Update:** NeuralHermes-2.5 became the best Hermes-based model on the Open LLM leaderboard and one of the very best 7b models. 🎉
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
### AGIEval
### GPT4All
### TruthfulQA
You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/DPO/runs/axe71gr0?nw=nwusermlabonne).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536
| {"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation", "dpo", "rlhf"], "datasets": ["mlabonne/chatml_dpo_pairs"], "base_model": "teknium/OpenHermes-2.5-Mistral-7B", "model-index": [{"name": "NeuralHermes-2.5-Mistral-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 66.55, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 84.9, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 63.32, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 54.93}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 78.3, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 61.33, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B", "name": "Open LLM Leaderboard"}}]}]} | jalaganapathy/jalaModelRepo | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"conversational",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:30:42+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #instruct #finetune #chatml #gpt4 #synthetic data #distillation #dpo #rlhf #conversational #en #dataset-mlabonne/chatml_dpo_pairs #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<center><img src="https://i.URL
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is based on the teknium/OpenHermes-2.5-Mistral-7B model that has been further fine-tuned with Direct Preference Optimization (DPO) using the mlabonne/chatml_dpo_pairs dataset. It surpasses the original model on most benchmarks (see results).
It is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on Google Colab and GitHub. It required an A100 GPU for about an hour.
## Quantized models
* GGUF: URL
* AWQ: URL
* GPTQ: URL
* EXL2:
* 3.0bpw: URL
* 4.0bpw: URL
* 5.0bpw: URL
* 6.0bpw: URL
* 8.0bpw: URL
## Results
Update: NeuralHermes-2.5 became the best Hermes-based model on the Open LLM leaderboard and one of the very best 7b models.
!image/png
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model (see his tweet).
Results are improved on every benchmark: AGIEval (from 43.07% to 43.62%), GPT4All (from 73.12% to 73.25%), and TruthfulQA.
### AGIEval
![](https://i.URL
### GPT4All
![](https://i.URL
### TruthfulQA
![](https://i.URL
You can check the Weights & Biases project here.
## Usage
You can run this model using LM Studio or any other frontend.
You can also run this model using the following code:
## Training hyperparameters
LoRA:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
Training arguments:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
DPOTrainer:
* beta=0.1
* max_prompt_length=1024
* max_length=1536
| [
"# NeuralHermes 2.5 - Mistral 7B\n\nNeuralHermes is based on the teknium/OpenHermes-2.5-Mistral-7B model that has been further fine-tuned with Direct Preference Optimization (DPO) using the mlabonne/chatml_dpo_pairs dataset. It surpasses the original model on most benchmarks (see results).\n\nIt is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.\n\nThe code to train this model is available on Google Colab and GitHub. It required an A100 GPU for about an hour.",
"## Quantized models\n\n* GGUF: URL\n* AWQ: URL\n* GPTQ: URL\n* EXL2:\n * 3.0bpw: URL\n * 4.0bpw: URL\n * 5.0bpw: URL\n * 6.0bpw: URL\n * 8.0bpw: URL",
"## Results\n\nUpdate: NeuralHermes-2.5 became the best Hermes-based model on the Open LLM leaderboard and one of the very best 7b models. \n\n!image/png\n\nTeknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model (see his tweet).\n\nResults are improved on every benchmark: AGIEval (from 43.07% to 43.62%), GPT4All (from 73.12% to 73.25%), and TruthfulQA.",
"### AGIEval\n![](https://i.URL",
"### GPT4All\n![](https://i.URL",
"### TruthfulQA\n![](https://i.URL\n\nYou can check the Weights & Biases project here.",
"## Usage\n\nYou can run this model using LM Studio or any other frontend.\n\nYou can also run this model using the following code:",
"## Training hyperparameters\n\nLoRA:\n* r=16\n* lora_alpha=16\n* lora_dropout=0.05\n* bias=\"none\"\n* task_type=\"CAUSAL_LM\"\n* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']\n\nTraining arguments:\n* per_device_train_batch_size=4\n* gradient_accumulation_steps=4\n* gradient_checkpointing=True\n* learning_rate=5e-5\n* lr_scheduler_type=\"cosine\"\n* max_steps=200\n* optim=\"paged_adamw_32bit\"\n* warmup_steps=100\n\nDPOTrainer:\n* beta=0.1\n* max_prompt_length=1024\n* max_length=1536"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #instruct #finetune #chatml #gpt4 #synthetic data #distillation #dpo #rlhf #conversational #en #dataset-mlabonne/chatml_dpo_pairs #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralHermes 2.5 - Mistral 7B\n\nNeuralHermes is based on the teknium/OpenHermes-2.5-Mistral-7B model that has been further fine-tuned with Direct Preference Optimization (DPO) using the mlabonne/chatml_dpo_pairs dataset. It surpasses the original model on most benchmarks (see results).\n\nIt is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.\n\nThe code to train this model is available on Google Colab and GitHub. It required an A100 GPU for about an hour.",
"## Quantized models\n\n* GGUF: URL\n* AWQ: URL\n* GPTQ: URL\n* EXL2:\n * 3.0bpw: URL\n * 4.0bpw: URL\n * 5.0bpw: URL\n * 6.0bpw: URL\n * 8.0bpw: URL",
"## Results\n\nUpdate: NeuralHermes-2.5 became the best Hermes-based model on the Open LLM leaderboard and one of the very best 7b models. \n\n!image/png\n\nTeknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model (see his tweet).\n\nResults are improved on every benchmark: AGIEval (from 43.07% to 43.62%), GPT4All (from 73.12% to 73.25%), and TruthfulQA.",
"### AGIEval\n![](https://i.URL",
"### GPT4All\n![](https://i.URL",
"### TruthfulQA\n![](https://i.URL\n\nYou can check the Weights & Biases project here.",
"## Usage\n\nYou can run this model using LM Studio or any other frontend.\n\nYou can also run this model using the following code:",
"## Training hyperparameters\n\nLoRA:\n* r=16\n* lora_alpha=16\n* lora_dropout=0.05\n* bias=\"none\"\n* task_type=\"CAUSAL_LM\"\n* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']\n\nTraining arguments:\n* per_device_train_batch_size=4\n* gradient_accumulation_steps=4\n* gradient_checkpointing=True\n* learning_rate=5e-5\n* lr_scheduler_type=\"cosine\"\n* max_steps=200\n* optim=\"paged_adamw_32bit\"\n* warmup_steps=100\n\nDPOTrainer:\n* beta=0.1\n* max_prompt_length=1024\n* max_length=1536"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) as a base.
### Models Merged
The following models were included in the merge:
* [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2)
* [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Nous-Hermes-2-Yi-34B
- model: jondurbin/bagel-dpo-34b-v0.2
parameters:
weight: 0.6
density: 0.6
merge_method: dare_ties
base_model: NousResearch/Nous-Hermes-2-Yi-34B
parameters:
normalize: true
int8_mask: true
dtype: float16
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["NousResearch/Nous-Hermes-2-Yi-34B", "jondurbin/bagel-dpo-34b-v0.2"]} | NotAiLOL/Boundary-Yi-34B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:NousResearch/Nous-Hermes-2-Yi-34B",
"base_model:jondurbin/bagel-dpo-34b-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:31:22+00:00 | [
"2311.03099",
"2306.01708"
] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #arxiv-2311.03099 #arxiv-2306.01708 #base_model-NousResearch/Nous-Hermes-2-Yi-34B #base_model-jondurbin/bagel-dpo-34b-v0.2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the DARE TIES merge method using NousResearch/Nous-Hermes-2-Yi-34B as a base.
### Models Merged
The following models were included in the merge:
* jondurbin/bagel-dpo-34b-v0.2
* NousResearch/Nous-Hermes-2-Yi-34B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using NousResearch/Nous-Hermes-2-Yi-34B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* jondurbin/bagel-dpo-34b-v0.2\n* NousResearch/Nous-Hermes-2-Yi-34B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #arxiv-2311.03099 #arxiv-2306.01708 #base_model-NousResearch/Nous-Hermes-2-Yi-34B #base_model-jondurbin/bagel-dpo-34b-v0.2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using NousResearch/Nous-Hermes-2-Yi-34B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* jondurbin/bagel-dpo-34b-v0.2\n* NousResearch/Nous-Hermes-2-Yi-34B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0_ablation_sample1_4iters_bs256_iter_3
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2](https://huggingface.co/ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2) on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset"], "base_model": "ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2", "model-index": [{"name": "0.0_ablation_sample1_4iters_bs256_iter_3", "results": []}]} | ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset",
"base_model:ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T13:33:37+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset #base_model-ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_sample1_4iters_bs256_iter_3
This model is a fine-tuned version of ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2 on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
"# 0.0_ablation_sample1_4iters_bs256_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2 on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset #base_model-ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 0.0_ablation_sample1_4iters_bs256_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_2 on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] |
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