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text-generation | transformers |
# Uploaded model
- **Developed by:** duxx
- **License:** apache-2.0
- **Finetuned from model :** duxx/rag-v.01
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": "duxx/rag-v.01"} | duxx/rag-v.02 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:duxx/rag-v.01",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T12:45:34+00:00 |
text2text-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. -->
# t5-sent-rebuilder-v2
This model is a fine-tuned version of [ai-forever/ruT5-base](https://huggingface.co/ai-forever/ruT5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7782
- Bleu: 44.0278
- Meteor: 0.6656
- Gen Len: 8.234
## 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: 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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.4487 | 1.0 | 1470 | 0.8752 | 38.0222 | 0.6229 | 8.1517 |
| 0.9863 | 2.0 | 2940 | 0.8036 | 41.364 | 0.6467 | 8.1911 |
| 0.7951 | 3.0 | 4410 | 0.7789 | 43.1979 | 0.6612 | 8.2431 |
| 0.6899 | 4.0 | 5880 | 0.7773 | 43.1943 | 0.6602 | 8.1772 |
| 0.6226 | 5.0 | 7350 | 0.7714 | 43.695 | 0.663 | 8.2343 |
| 0.5762 | 6.0 | 8820 | 0.7782 | 44.0278 | 0.6656 | 8.234 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "ai-forever/ruT5-base", "model-index": [{"name": "t5-sent-rebuilder-v2", "results": []}]} | XvKuoMing/t5-sent-rebuilder-v2 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ai-forever/ruT5-base",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T12:45:50+00:00 |
null | null | {} | 9408257033sgjktkktmtnt/9408257033 | null | [
"region:us"
] | null | 2024-05-03T12:46:18+00:00 |
|
null | null | {"license": "apache-2.0", "datasets": ["HuggingFaceFW/fineweb"]} | Sashiandr/Nnnn1 | null | [
"dataset:HuggingFaceFW/fineweb",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T12:46:43+00:00 |
|
image-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. -->
# batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Accuracy: 0.9718
- Recall: 0.8900
- Precision: 0.9781
- F1: 0.9320
- Roc Auc: 0.9977
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.059 | 1.0 | 1377 | 0.0768 | 0.9718 | 0.8900 | 0.9781 | 0.9320 | 0.9977 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "recall", "precision", "f1"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9717747424785588, "name": "Accuracy"}, {"type": "recall", "value": 0.8900083524744206, "name": "Recall"}, {"type": "precision", "value": 0.9781411359724613, "name": "Precision"}, {"type": "f1", "value": 0.9319958454053463, "name": "F1"}]}]}]} | hchcsuim/batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T12:46:46+00:00 |
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. -->
# gemma-7b-request_to_configuration
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/gemma-7b", "model-index": [{"name": "gemma-7b-request_to_configuration", "results": []}]} | simengel/gemma-7b-request_to_configuration | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-7b",
"license:gemma",
"region:us"
] | null | 2024-05-03T12:49:26+00:00 |
null | null | {} | kiwiee/randy | null | [
"region:us"
] | null | 2024-05-03T12:49:29+00:00 |
|
null | null | {} | nncyberpunk/LoRA_SDXL1.0_SinfullyStylish_SDXL | null | [
"region:us"
] | null | 2024-05-03T12:50:22+00:00 |
|
text2text-generation | transformers | {} | GiGi2044/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T12:50:57+00:00 |
|
null | null | {} | harshh1307/user_dish_rec1_model | null | [
"region:us"
] | null | 2024-05-03T12:51:16+00:00 |
|
null | transformers | {} | aslon1213/whisper-small-uz-with-uzbekvoice-ct2 | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T12:51:34+00:00 |
|
null | null | {} | Er-MohammedAfnan/simple-calculator-by-mohammed-afnan | null | [
"region:us"
] | null | 2024-05-03T12:52:20+00:00 |
|
reinforcement-learning | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: elisamammi/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | elisamammi/poca-SoccerTwos | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | null | 2024-05-03T12:52:33+00:00 |
null | null | {"license": "cc"} | Mirman619/Mfloraldress | null | [
"license:cc",
"region:us"
] | null | 2024-05-03T12:53:10+00:00 |
|
null | null | {} | eddieman78/events-mem-large-gcp | null | [
"region:us"
] | null | 2024-05-03T12:53:18+00:00 |
|
null | null | {} | itsankitdas/sd-dress-model-lora-sdxl | null | [
"region:us"
] | null | 2024-05-03T12:53:36+00:00 |
|
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": []} | golf2248/060g7wb | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T12:54:08+00:00 |
text-generation | transformers |
<br/><br/>
8bpw/h8 exl2 quantization of [Endevor/InfinityRP-v2-8B](https://huggingface.co/Endevor/InfinityRP-v2-8B) using default exllamav2 calibration dataset.
---
**ORIGINAL CARD:**
The idea is the same as [InfinityRP v1](https://huggingface.co/Endevor/InfinityRP-v1-7B), but this one is Llama 3 with 16k ctx! Have fun...
### Prompt format: Alpaca.
``"You are now in roleplay chat mode. Engage in an endless chat, always with a creative response. Follow lengths very precisely and create paragraphs accurately. Always wait your turn, next actions and responses. Your internal thoughts are wrapped with ` marks."``
**User Message Prefix = ### Input:**
**Assistant Message Prefix = ### Response:**
**System Message Prefix = ### Instruction:**
**Turn on "Include Names"** (optional)
### Text Length: (use on your System Prompt or ### Response:)
Response: (length = medium) <- [tiny, micro, short, medium, long, enormous, huge, massive, humongous]
### Example:
 | {"language": ["en"], "license": "apache-2.0", "tags": ["safetensors", "llama", "not-for-all-audiences", "nsfw", "rp", "roleplay"], "pipeline_tag": "text-generation"} | JayhC/InfinityRP-v2-8B-8bpw-h8-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"rp",
"roleplay",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T12:54:10+00:00 |
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)
deepseek-coder-7b-instruct-v1.5 - bnb 8bits
- Model creator: https://huggingface.co/deepseek-ai/
- Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5/
Original model description:
---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5
Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 2. Evaluation Results
<img width="1000px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/6538815d1bdb3c40db94fbfa/xOtCTW5xdoLCKY4FR6tri.png">
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
| {} | RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T12:54:51+00:00 |
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. -->
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| {"library_name": "transformers", "tags": []} | golf2248/4fms3oz | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T12:58:10+00:00 |
text-generation | transformers |
# karasu-moe
karasu-moe is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [niryuu/Karasu-1.1b-chat-vector](https://huggingface.co/niryuu/Karasu-1.1b-chat-vector)
* [lightblue/karasu-1.1B](https://huggingface.co/lightblue/karasu-1.1B)
## 🧩 Configuration
```yaml
base_model: niryuu/Karasu-1.1b-chat-vector
experts:
- source_model: niryuu/Karasu-1.1b-chat-vector
positive_prompts:
- "chat"
- "assistant"
- "explain"
- source_model: lightblue/karasu-1.1B
positive_prompts:
- "reason"
- "instruct"
- "count"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/karasu-moe"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "niryuu/Karasu-1.1b-chat-vector", "lightblue/karasu-1.1B"], "base_model": ["niryuu/Karasu-1.1b-chat-vector", "lightblue/karasu-1.1B"]} | aipib/karasu-moe | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"niryuu/Karasu-1.1b-chat-vector",
"lightblue/karasu-1.1B",
"conversational",
"base_model:niryuu/Karasu-1.1b-chat-vector",
"base_model:lightblue/karasu-1.1B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:01:15+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** VinhLlama
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b
This gemma 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", "gemma", "trl"], "base_model": "unsloth/gemma-2b"} | VinhLlama/Gemma2bVinhntV02 | null | [
"transformers",
"pytorch",
"safetensors",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/gemma-2b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:01:29+00:00 |
null | null | {"license": "apache-2.0"} | markleschinsky/testmodel | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:02:25+00:00 |
|
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | cdactvm/w2v-bert-2.0-unified_speech_cls.v1 | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:02:28+00:00 |
null | null | {} | bokovai/KAN | null | [
"region:us"
] | null | 2024-05-03T13:03:20+00:00 |
|
text-generation | transformers | {} | VictorDCh/Llama-3-8B-Instruct-spider_full | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:03:42+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOpeepeepoopoo/herewegoagain13c | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:04:38+00:00 |
automatic-speech-recognition | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | ezyyeah/murix-large-v2-100steps-MERGED | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:05:26+00:00 |
null | transformers |
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_4_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | 2024-05-03T13:06:09+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | cilantro9246/ywf6ss2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:06:17+00:00 |
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed] | {"library_name": "transformers", "tags": []} | aashish-249/Emotion_classification | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:06:24+00:00 |
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. -->
# loha_fine_tuned_copa
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6922
- Accuracy: 0.5
- F1: 0.5
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| 0.7089 | 1.0 | 50 | 0.6923 | 0.5 | 0.5 |
| 0.7026 | 2.0 | 100 | 0.6923 | 0.5 | 0.5 |
| 0.6995 | 3.0 | 150 | 0.6923 | 0.5 | 0.5 |
| 0.7009 | 4.0 | 200 | 0.6923 | 0.5 | 0.5 |
| 0.6997 | 5.0 | 250 | 0.6923 | 0.5 | 0.5 |
| 0.7028 | 6.0 | 300 | 0.6922 | 0.5 | 0.5 |
| 0.6951 | 7.0 | 350 | 0.6922 | 0.5 | 0.5 |
| 0.6949 | 8.0 | 400 | 0.6922 | 0.5 | 0.5 |
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "loha_fine_tuned_copa", "results": []}]} | lenatr99/loha_fine_tuned_copa | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:07:09+00:00 |
null | transformers |
# nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF
This model was converted to GGUF format from [`nonsonpratico/phi3-3.8-128k-italian-v2`](https://huggingface.co/nonsonpratico/phi3-3.8-128k-italian-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nonsonpratico/phi3-3.8-128k-italian-v2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF --model phi3-3.8-128k-italian-v2.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF --model phi3-3.8-128k-italian-v2.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi3-3.8-128k-italian-v2.Q4_K_M.gguf -n 128
```
| {"language": ["it"], "license": "apache-2.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["cosimoiaia/Loquace-102k"]} | nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF | null | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"it",
"dataset:cosimoiaia/Loquace-102k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:11:50+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_8_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:12:18+00:00 |
null | null | # lina-speech (beta)
Exploring "linear attention" for text-to-speech.
It predicts audio codec "à la" [MusicGen](https://arxiv.org/abs/2306.05284) : delayed residual vector quantizers so that we do not need multiple models.
Featuring [RWKV](https://github.com/BlinkDL/RWKV-LM), [Mamba](https://github.com/state-spaces/mamba), [Gated Linear Attention](https://github.com/sustcsonglin/flash-linear-attention).
Compared to other LM TTS model :
- Can be easily pretrained and finetuned on midrange GPUs.
- Tiny memory footprint.
- Trained on long context (up to 2000 tokens : ~27s).
### Models
| Model | #Params | Dataset | Checkpoint | Steps | Note |
| :---: | :---: |:---: |:---: |:---: |:---: |
| GLA | 60M, 130M | Librilight-medium | [Download](https://nubo.ircam.fr/index.php/s/wjNYLb54m7L8xf9) | 300k | GPU inference only |
| Mamba| 60M | Librilight-medium |[Download](https://nubo.ircam.fr/index.php/s/wjNYLb54m7L8xf9)| 300k | GPU inference only |
| RWKV v6 | 60M | LibriTTS |[Download](https://nubo.ircam.fr/index.php/s/wjNYLb54m7L8xf9) | 150k | GPU inference only |
### Installation
Following the linear complexity LM you choose, follow respective instructions first:
- For Mamba check the [official repo](https://github.com/state-spaces/mamba).
- For GLA/RWKV inference check [flash-linear-attention](https://github.com/sustcsonglin/flash-linear-attention).
- For RWKV training check [RWKV-LM](https://github.com/BlinkDL/RWKV-LM)
### Acknowledgment
- The RWKV authors and the community around for carrying high-level truly opensource research.
- @SmerkyG for making my life easy at testing cutting edge language model.
- @lucidrains for its huge codebase.
- @sustcsonglin who made [GLA and FLA](https://github.com/sustcsonglin/flash-linear-attention).
- @harrisonvanderbyl fixing RWKV inference.
### Cite
```bib
@software{lemerle2024linaspeech,
title = {LinaSpeech: Exploring "linear attention" for text-to-speech.},
author = {Lemerle, Théodor},
url = {https://github.com/theodorblackbird/lina-speech},
month = april,
year = {2024}
}
```
### IRCAM
This work takes place at IRCAM, and is part of the following project :
[ANR Exovoices](https://anr.fr/Projet-ANR-21-CE23-0040)
<img align="left" width="200" height="200" src="logo_ircam.jpeg"> | {"license": "cc-by-nc-4.0"} | lina-speech/all-models | null | [
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-05-03T13:12:32+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
mid level trained on Jack O'Neill request response
## 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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Users (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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[More Information Needed]
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[More Information Needed]
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[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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[More Information Needed] | {"library_name": "transformers", "tags": []} | nachtwindecho/mistralai-Code-Instruct-Finetune-SG1-V4 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:15:10+00:00 |
null | null | {"license": "mit"} | rukeshduwal/translaor | null | [
"license:mit",
"region:us"
] | null | 2024-05-03T13:15:10+00:00 |
|
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)
deepseek-coder-7b-instruct-v1.5 - GGUF
- Model creator: https://huggingface.co/deepseek-ai/
- Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [deepseek-coder-7b-instruct-v1.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q2_K.gguf) | Q2_K | 2.53GB |
| [deepseek-coder-7b-instruct-v1.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ3_XS.gguf) | IQ3_XS | 2.79GB |
| [deepseek-coder-7b-instruct-v1.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ3_S.gguf) | IQ3_S | 2.92GB |
| [deepseek-coder-7b-instruct-v1.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K_S.gguf) | Q3_K_S | 2.92GB |
| [deepseek-coder-7b-instruct-v1.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [deepseek-coder-7b-instruct-v1.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K.gguf) | Q3_K | 3.22GB |
| [deepseek-coder-7b-instruct-v1.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K_M.gguf) | Q3_K_M | 3.22GB |
| [deepseek-coder-7b-instruct-v1.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K_L.gguf) | Q3_K_L | 3.49GB |
| [deepseek-coder-7b-instruct-v1.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ4_XS.gguf) | IQ4_XS | 3.56GB |
| [deepseek-coder-7b-instruct-v1.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_0.gguf) | Q4_0 | 3.73GB |
| [deepseek-coder-7b-instruct-v1.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ4_NL.gguf) | IQ4_NL | 3.74GB |
| [deepseek-coder-7b-instruct-v1.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_K_S.gguf) | Q4_K_S | 3.75GB |
| [deepseek-coder-7b-instruct-v1.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_K.gguf) | Q4_K | 3.93GB |
| [deepseek-coder-7b-instruct-v1.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_K_M.gguf) | Q4_K_M | 3.93GB |
| [deepseek-coder-7b-instruct-v1.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_1.gguf) | Q4_1 | 4.1GB |
| [deepseek-coder-7b-instruct-v1.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_0.gguf) | Q5_0 | 4.48GB |
| [deepseek-coder-7b-instruct-v1.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_K_S.gguf) | Q5_K_S | 4.48GB |
| [deepseek-coder-7b-instruct-v1.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_K.gguf) | Q5_K | 4.59GB |
| [deepseek-coder-7b-instruct-v1.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_K_M.gguf) | Q5_K_M | 4.59GB |
| [deepseek-coder-7b-instruct-v1.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_1.gguf) | Q5_1 | 4.86GB |
| [deepseek-coder-7b-instruct-v1.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q6_K.gguf) | Q6_K | 5.28GB |
Original model description:
---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5
Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 2. Evaluation Results
<img width="1000px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/6538815d1bdb3c40db94fbfa/xOtCTW5xdoLCKY4FR6tri.png">
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
| {} | RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-03T13:15:35+00:00 |
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)
starcoder2-15b - bnb 4bits
- Model creator: https://huggingface.co/bigcode/
- Original model: https://huggingface.co/bigcode/starcoder2-15b/
Original model description:
---
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.2
top_p: 0.95
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
datasets:
- bigcode/the-stack-v2-train
license: bigcode-openrail-m
library_name: transformers
tags:
- code
model-index:
- name: starcoder2-15b
results:
- task:
type: text-generation
dataset:
name: CruxEval-I
type: cruxeval-i
metrics:
- type: pass@1
value: 48.1
- task:
type: text-generation
dataset:
name: DS-1000
type: ds-1000
metrics:
- type: pass@1
value: 33.8
- task:
type: text-generation
dataset:
name: GSM8K (PAL)
type: gsm8k-pal
metrics:
- type: accuracy
value: 65.1
- task:
type: text-generation
dataset:
name: HumanEval+
type: humanevalplus
metrics:
- type: pass@1
value: 37.8
- task:
type: text-generation
dataset:
name: HumanEval
type: humaneval
metrics:
- type: pass@1
value: 46.3
- task:
type: text-generation
dataset:
name: RepoBench-v1.1
type: repobench-v1.1
metrics:
- type: edit-smiliarity
value: 74.08
---
# StarCoder2
<center>
<img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600">
</center>
## Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [License](#license)
6. [Citation](#citation)
## Model Summary
StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train), with opt-out requests excluded. The model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245), [a context window of 16,384 tokens](https://arxiv.org/abs/2205.14135) with [a sliding window attention of 4,096 tokens](https://arxiv.org/abs/2004.05150v2), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 4+ trillion tokens.
The model was trained with [NVIDIA NeMo™ Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the [NVIDIA Eos Supercomputer](https://blogs.nvidia.com/blog/eos/) built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems.
- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
- **Paper:** [Link](https://huggingface.co/papers/2402.19173)
- **Point of Contact:** [[email protected]](mailto:[email protected])
- **Languages:** 600+ Programming languages
## Use
### Intended use
The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well.
### Generation
Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's [GitHub repository](https://github.com/bigcode-project/starcoder2).
First, make sure to install `transformers` from source:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
#### Running the model on CPU/GPU/multi GPU
* _Using full precision_
```python
# pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/starcoder2-15b"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "bigcode/starcoder2-15b"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for fp16 use `torch_dtype=torch.float16` instead
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 32251.33 MB
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
# to use 4bit use `load_in_4bit=True` instead
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
checkpoint = "bigcode/starcoder2-15b"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
# load_in_8bit
Memory footprint: 16900.18 MB
# load_in_4bit
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 9224.60 MB
```
### Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search-v2) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
# Limitations
The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations.
# Training
## Model
- **Architecture:** Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective
- **Pretraining steps:** 1 million
- **Pretraining tokens:** 4+ trillion
- **Precision:** bfloat16
## Hardware
- **GPUs:** 1024 x H100
## Software
- **Framework:** [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
# License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# Citation
```bash
@misc{lozhkov2024starcoder,
title={StarCoder 2 and The Stack v2: The Next Generation},
author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
year={2024},
eprint={2402.19173},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
```
| {} | RichardErkhov/bigcode_-_starcoder2-15b-4bits | null | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"arxiv:2305.13245",
"arxiv:2205.14135",
"arxiv:2004.05150",
"arxiv:2207.14255",
"arxiv:2402.19173",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T13:15:59+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Shanwun
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
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", "gguf"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | Shanwun/uncdrew-gguf | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:16:13+00:00 |
null | transformers |
# Model Card for Model ID
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## 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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## Model Examination [optional]
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## Environmental Impact
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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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## Technical Specifications [optional]
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## More Information [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | rohangbs/death_se42-type2_May2024 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:16:21+00:00 |
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-numbers-augmented2
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9722
- Accuracy: 0.3269
- F1: 0.2716
- Precision: 0.3970
- Recall: 0.3277
## 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-06
- 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: 2816
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.2279 | 0.2504 | 705 | 2.2645 | 0.1824 | 0.1262 | 0.2559 | 0.1792 |
| 1.7024 | 1.25 | 1409 | 2.0462 | 0.3167 | 0.2828 | 0.3354 | 0.3152 |
| 1.3164 | 2.25 | 2113 | 1.9759 | 0.3081 | 0.2568 | 0.3022 | 0.3085 |
| 1.3877 | 3.2496 | 2816 | 1.9641 | 0.3373 | 0.2839 | 0.3031 | 0.3367 |
### 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", "f1", "precision", "recall"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-numbers-augmented2", "results": []}]} | latif98/videomae-base-finetuned-numbers-augmented2 | null | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:17:09+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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[More Information Needed]
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#### Preprocessing [optional]
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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": []} | golf2248/sjrh9dx | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:17:09+00:00 |
null | null | {} | MohametSena/sd-naruto-model | null | [
"region:us"
] | null | 2024-05-03T13:17:47+00:00 |
|
null | null | {} | ttc0000/mistral_HFTrainer_instruct02_Sample1_lora_r64_a128_optim32bit | null | [
"safetensors",
"region:us"
] | null | 2024-05-03T13:17:58+00:00 |
|
null | null | {} | onelock/distilbert-base-uncased-finetuned-nf-2nd-edition | null | [
"region:us"
] | null | 2024-05-03T13:20:26+00:00 |
|
audio-to-audio | adapter-transformers | {"language": ["as"], "license": "apache-2.0", "library_name": "adapter-transformers", "tags": ["medical"], "datasets": ["openbmb/UltraInteract_sft"], "metrics": ["code_eval"], "pipeline_tag": "audio-to-audio"} | geovanio/caoselvagem | null | [
"adapter-transformers",
"medical",
"audio-to-audio",
"as",
"dataset:openbmb/UltraInteract_sft",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:20:32+00:00 |
|
text-to-speech | null | pretrained models used in https://github.com/RVC-Boss/GPT-SoVITS | {"license": "mit", "pipeline_tag": "text-to-speech"} | blaise-tk/GPT-SoVITS-Fork | null | [
"text-to-speech",
"license:mit",
"region:us"
] | null | 2024-05-03T13:20:38+00:00 |
null | null | {} | bakkensus/phi-2-rout40-silver40-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-03T13:20:47+00:00 |
|
null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.01_2_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:20:47+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | golf2248/uv50wfi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:22:22+00:00 |
null | transformers |
# Model Card for Model ID
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | megajajo/phi-1_5-finetuned-kotlin-completion | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:23:58+00:00 |
text-classification | adapter-transformers | {"license": "mit", "library_name": "adapter-transformers", "pipeline_tag": "text-classification"} | ShawnSuuun/toxity_CNN_F | null | [
"adapter-transformers",
"keras",
"text-classification",
"license:mit",
"region:us"
] | null | 2024-05-03T13:24:00+00:00 |
|
null | null | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/WEMAKE-CX/Llama-3-8B-Instruct-V41-1048k
| {} | mradermacher/Llama-3-8B-Instruct-V41-1048k-i1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-03T13:24:05+00:00 |
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. -->
# loha_fine_tuned_cb
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3194
- Accuracy: 0.3182
- F1: 0.1536
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 1.0888 | 3.5714 | 50 | 1.0482 | 0.2727 | 0.1364 |
| 0.8883 | 7.1429 | 100 | 1.1156 | 0.3182 | 0.1536 |
| 0.808 | 10.7143 | 150 | 1.1921 | 0.3182 | 0.1536 |
| 0.732 | 14.2857 | 200 | 1.2529 | 0.3182 | 0.1536 |
| 0.7553 | 17.8571 | 250 | 1.2877 | 0.3182 | 0.1536 |
| 0.7436 | 21.4286 | 300 | 1.3078 | 0.3182 | 0.1536 |
| 0.7115 | 25.0 | 350 | 1.3167 | 0.3182 | 0.1536 |
| 0.7395 | 28.5714 | 400 | 1.3194 | 0.3182 | 0.1536 |
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "loha_fine_tuned_cb", "results": []}]} | lenatr99/loha_fine_tuned_cb | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:24:57+00:00 |
null | null | {"license": "apache-2.0"} | DongKyung/InstructPix2Pix | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:25:10+00:00 |
|
null | null | {"license": "mit"} | SsharvienKumar/HARP_Models | null | [
"license:mit",
"region:us"
] | null | 2024-05-03T13:25:27+00:00 |
|
null | null | {} | Cesco2004/TEST.V3 | null | [
"region:us"
] | null | 2024-05-03T13:26:35+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/adamo1139/Yi-34B-200K-XLCTX
<!-- 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/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q2_K.gguf) | Q2_K | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ3_XS.gguf) | IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q3_K_S.gguf) | Q3_K_S | 15.1 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ3_M.gguf) | IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q3_K_L.gguf) | Q3_K_L | 18.2 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ4_XS.gguf) | IQ4_XS | 18.7 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q5_K_S.gguf) | Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q5_K_M.gguf) | Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q6_K.gguf) | Q6_K | 28.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q8_0.gguf) | Q8_0 | 36.6 | 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": "other", "library_name": "transformers", "base_model": "adamo1139/Yi-34B-200K-XLCTX", "license_link": "LICENSE", "license_name": "yi-license", "quantized_by": "mradermacher"} | mradermacher/Yi-34B-200K-XLCTX-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:adamo1139/Yi-34B-200K-XLCTX",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:26:50+00:00 |
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
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[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. -->
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## Evaluation
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### 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
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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]
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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 [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.01_4_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:28:06+00:00 |
null | null | {} | PierLo/kal_psxau | null | [
"region:us"
] | null | 2024-05-03T13:28:28+00:00 |
|
text-generation | transformers |
# Mistral-7b-Instruct-v0.1-fp16-ov
* Model creator: [Mistral AI](https://huggingface.co/mistralai)
* Original model: [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
## Description
This is [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16.
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2024.1.0 and higher
* Optimum Intel 1.16.0 and higher
## Running Model Inference
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "OpenVINO/mistral-7b-instrcut-v0.1-fp16-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
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")
outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
## Limitations
Check the original model card for [limitations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#limitations).
## Legal information
The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). | {"language": ["en"], "license": "apache-2.0"} | OpenVINO/mistral-7b-instruct-v0.1-fp16-ov | null | [
"transformers",
"openvino",
"mistral",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:29:02+00:00 |
null | null | {} | onelock/bert-base-swedish-cased-finetuned-nf-2nd-edition | null | [
"region:us"
] | null | 2024-05-03T13:29:05+00:00 |
|
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": []} | OwOpeepeepoopoo/herewegoagain14 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:29:10+00:00 |
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)
starcoder2-15b - GGUF
- Model creator: https://huggingface.co/bigcode/
- Original model: https://huggingface.co/bigcode/starcoder2-15b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [starcoder2-15b.Q2_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q2_K.gguf) | Q2_K | 5.77GB |
| [starcoder2-15b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ3_XS.gguf) | IQ3_XS | 6.25GB |
| [starcoder2-15b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ3_S.gguf) | IQ3_S | 6.52GB |
| [starcoder2-15b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K_S.gguf) | Q3_K_S | 6.51GB |
| [starcoder2-15b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ3_M.gguf) | IQ3_M | 6.8GB |
| [starcoder2-15b.Q3_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K.gguf) | Q3_K | 7.49GB |
| [starcoder2-15b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K_M.gguf) | Q3_K_M | 7.49GB |
| [starcoder2-15b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K_L.gguf) | Q3_K_L | 8.35GB |
| [starcoder2-15b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ4_XS.gguf) | IQ4_XS | 8.12GB |
| [starcoder2-15b.Q4_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_0.gguf) | Q4_0 | 8.44GB |
| [starcoder2-15b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ4_NL.gguf) | IQ4_NL | 8.55GB |
| [starcoder2-15b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_K_S.gguf) | Q4_K_S | 8.53GB |
| [starcoder2-15b.Q4_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_K.gguf) | Q4_K | 9.18GB |
| [starcoder2-15b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_K_M.gguf) | Q4_K_M | 9.18GB |
| [starcoder2-15b.Q4_1.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_1.gguf) | Q4_1 | 9.35GB |
| [starcoder2-15b.Q5_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_0.gguf) | Q5_0 | 10.27GB |
| [starcoder2-15b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_K_S.gguf) | Q5_K_S | 10.27GB |
| [starcoder2-15b.Q5_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_K.gguf) | Q5_K | 10.65GB |
| [starcoder2-15b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_K_M.gguf) | Q5_K_M | 10.65GB |
| [starcoder2-15b.Q5_1.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_1.gguf) | Q5_1 | 11.18GB |
| [starcoder2-15b.Q6_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q6_K.gguf) | Q6_K | 12.2GB |
Original model description:
---
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.2
top_p: 0.95
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
datasets:
- bigcode/the-stack-v2-train
license: bigcode-openrail-m
library_name: transformers
tags:
- code
model-index:
- name: starcoder2-15b
results:
- task:
type: text-generation
dataset:
name: CruxEval-I
type: cruxeval-i
metrics:
- type: pass@1
value: 48.1
- task:
type: text-generation
dataset:
name: DS-1000
type: ds-1000
metrics:
- type: pass@1
value: 33.8
- task:
type: text-generation
dataset:
name: GSM8K (PAL)
type: gsm8k-pal
metrics:
- type: accuracy
value: 65.1
- task:
type: text-generation
dataset:
name: HumanEval+
type: humanevalplus
metrics:
- type: pass@1
value: 37.8
- task:
type: text-generation
dataset:
name: HumanEval
type: humaneval
metrics:
- type: pass@1
value: 46.3
- task:
type: text-generation
dataset:
name: RepoBench-v1.1
type: repobench-v1.1
metrics:
- type: edit-smiliarity
value: 74.08
---
# StarCoder2
<center>
<img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600">
</center>
## Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [License](#license)
6. [Citation](#citation)
## Model Summary
StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train), with opt-out requests excluded. The model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245), [a context window of 16,384 tokens](https://arxiv.org/abs/2205.14135) with [a sliding window attention of 4,096 tokens](https://arxiv.org/abs/2004.05150v2), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 4+ trillion tokens.
The model was trained with [NVIDIA NeMo™ Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the [NVIDIA Eos Supercomputer](https://blogs.nvidia.com/blog/eos/) built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems.
- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
- **Paper:** [Link](https://huggingface.co/papers/2402.19173)
- **Point of Contact:** [[email protected]](mailto:[email protected])
- **Languages:** 600+ Programming languages
## Use
### Intended use
The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well.
### Generation
Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's [GitHub repository](https://github.com/bigcode-project/starcoder2).
First, make sure to install `transformers` from source:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
#### Running the model on CPU/GPU/multi GPU
* _Using full precision_
```python
# pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/starcoder2-15b"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "bigcode/starcoder2-15b"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for fp16 use `torch_dtype=torch.float16` instead
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 32251.33 MB
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
# to use 4bit use `load_in_4bit=True` instead
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
checkpoint = "bigcode/starcoder2-15b"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
```bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
# load_in_8bit
Memory footprint: 16900.18 MB
# load_in_4bit
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 9224.60 MB
```
### Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search-v2) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
# Limitations
The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations.
# Training
## Model
- **Architecture:** Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective
- **Pretraining steps:** 1 million
- **Pretraining tokens:** 4+ trillion
- **Precision:** bfloat16
## Hardware
- **GPUs:** 1024 x H100
## Software
- **Framework:** [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
# License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# Citation
```bash
@misc{lozhkov2024starcoder,
title={StarCoder 2 and The Stack v2: The Next Generation},
author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
year={2024},
eprint={2402.19173},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
```
| {} | RichardErkhov/bigcode_-_starcoder2-15b-gguf | null | [
"gguf",
"arxiv:2305.13245",
"arxiv:2205.14135",
"arxiv:2004.05150",
"arxiv:2207.14255",
"arxiv:2402.19173",
"region:us"
] | null | 2024-05-03T13:30:03+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model56 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:30:26+00:00 |
text-generation | transformers |
# Model Card for Model ID
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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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## Bias, Risks, and Limitations
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### Recommendations
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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
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[More Information Needed] | {"library_name": "transformers", "tags": []} | mostlyai/datallm-v2-meta-llama-3-8b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:32:31+00:00 |
text-classification | transformers | {} | theobaur/relevancy_classification_FEVER | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:33:12+00:00 |
|
text-generation | transformers |
<br/><br/>
3.2bpw/h6 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-70B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) using default exllamav2 calibration dataset.
---
**ORIGINAL CARD:**
## Lumimaid 0.1
<center><div style="width: 100%;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;">
</div></center>
This model uses the Llama3 **prompting format**
Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough.
We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data.
This model includes the new Luminae dataset from Ikari.
If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY).
## Credits:
- Undi
- IkariDev
## Description
This repo contains FP16 files of Lumimaid-70B-v0.1.
Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt)
## Training data used:
- [Aesir datasets](https://huggingface.co/MinervaAI)
- [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt)
- [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx
- [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt)
- [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal)
- Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset
- [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly)
- [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly)
- [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly)
- Airoboros (reduced)
- [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced)
## Models used (only for 8B)
- Initial LumiMaid 8B Finetune
- Undi95/Llama-3-Unholy-8B-e4
- Undi95/Llama-3-LewdPlay-8B
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
## Others
Undi: If you want to support us, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]} | JayhC/Llama-3-Lumimaid-70B-v0.1-3.2bpw-h6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:33:43+00:00 |
null | null | {} | Devano/natasha-hsr | null | [
"region:us"
] | null | 2024-05-03T13:33:47+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** yadz45
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
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": ["fr"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "datasets": ["yadz45/V2"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | yadz45/IA_V1-1 | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"fr",
"dataset:yadz45/V2",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:34:15+00:00 |
text-generation | transformers |
# Llama3-ChatQA-1.5-8B-256K
I tried to achive long context RAG pipeline with this model but I have very limited resources to test this workflow. Keep in mind that this is an experimentation.
This model is an 'amalgamation' of `winglian/llama-3-8b-256k-PoSE` and `nvidia/Llama3-ChatQA-1.5-8B`.
## Recipe
First I extracted the Lora adapter from `nvidia/Llama3-ChatQA-1.5-8B` using `mergekkit`. You can find the adapter [here](https://huggingface.co/beratcmn/Llama3-ChatQA-1.5-8B-lora).
After the extraction I merged the adapter with the `winglian/llama-3-8b-256k-PoSE` model.
## Prompt Format
Since base model wasn't finetuned for any specific format we can use the ChatQA's chat format.
```text
System: {System}
{Context}
User: {Question}
Assistant: {Response}
User: {Question}
Assistant:
```
Big thanks to Meta Team, Nvidia Team and of course Wing Lian.
## Notes
This model has not been tested on any benchmarks due to compute limitations. Base model wasn't evaluated using `Needle in Haystack` as well. There is a big possibility that this model might perform worse than both of the original models. | {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["mergekit", "peft", "nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "base_model": ["meta-llama/Meta-Llama-3-8B", "nvidia/Llama3-ChatQA-1.5-8B", "winglian/llama-3-8b-256k-PoSE"], "pipeline_tag": "text-generation"} | beratcmn/Llama3-ChatQA-1.5-8B-256K | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"peft",
"nvidia",
"chatqa-1.5",
"chatqa",
"llama-3",
"pytorch",
"en",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:34:23+00:00 |
null | null | {} | Zefirkefir/sn25-5-2 | null | [
"region:us"
] | null | 2024-05-03T13:34:26+00:00 |
|
null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.01_8_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:35:18+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | cilantro9246/0wgp5uk | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:35:48+00:00 |
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": "266.23 +/- 20.17", "name": "mean_reward", "verified": false}]}]}]} | Fetanos/Reinforcement_Learning | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-03T13:37:58+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | massimilianowosz/Llama-3-8B-instruct-Japanese-Chef | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:38:14+00:00 |
null | null | {} | Paral1ax/model1 | null | [
"region:us"
] | null | 2024-05-03T13:38:30+00:00 |
|
null | null | {} | tmerkh/hugging_transformies | null | [
"region:us"
] | null | 2024-05-03T13:38:58+00:00 |
|
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-7B-Instruct-v0.2-finetune-SWE_70_30_EN
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0943
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8789 | 1.0 | 1443 | 1.4980 |
| 1.141 | 2.0 | 2886 | 1.5139 |
| 0.941 | 3.0 | 4329 | 1.6951 |
| 1.2378 | 4.0 | 5772 | 1.8812 |
| 0.3967 | 5.0 | 7215 | 2.0943 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-finetune-SWE_70_30_EN", "results": []}]} | JuanjoLopez19/Mistral-7B-Instruct-v0.2-finetune-SWE_70_30_EN | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:39:25+00:00 |
null | transformers |
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | wuzhongyanqiu/code-search-net-tokenizer | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:40:14+00:00 |
null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
| {"license": "apache-2.0", "title": "Pneumonia Detection", "emoji": "\ud83c\udfe2", "colorFrom": "blue", "colorTo": "red", "sdk": "gradio", "sdk_version": "4.25.0", "app_file": "app.py", "pinned": false} | jeysshon/Isatron_V2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T13:40:26+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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 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.
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[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": []} | golf2248/xdxodp6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:41:10+00:00 |
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="Dat1710/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}]}]}]} | Dat1710/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-05-03T13:41:48+00:00 |
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. -->
# Llama-3-8B-Instruct-MoE-spider_full
This model is a fine-tuned version of [VictorDCh/Llama-3-8B-Instruct-MoE](https://huggingface.co/VictorDCh/Llama-3-8B-Instruct-MoE) on the generator 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
| {"tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "VictorDCh/Llama-3-8B-Instruct-MoE", "model-index": [{"name": "Llama-3-8B-Instruct-MoE-spider_full", "results": []}]} | VictorDCh/Llama-3-8B-Instruct-MoE-spider_full | null | [
"transformers",
"tensorboard",
"safetensors",
"mixtral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:VictorDCh/Llama-3-8B-Instruct-MoE",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:41:55+00:00 |
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": []} | rohangbs/death_se42-type2_May2024_Second | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:43:03+00:00 |
text-generation | transformers |
# Mistral-7b-Instruct-v0.1-int8-ov
* Model creator: [Mistral AI](https://huggingface.co/mistralai)
* Original model: [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
## Description
This is [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT8_ASYM**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html)
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2024.1.0 and higher
* Optimum Intel 1.16.0 and higher
## Running Model Inference
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "OepnVINO/mistral-7b-instrcut-v0.1-int8-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
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")
outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
## Limitations
Check the original model card for [limitations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#limitations).
## Legal information
The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). | {"language": ["en"], "license": "apache-2.0"} | OpenVINO/mistral-7b-instrcut-v0.1-int8-ov | null | [
"transformers",
"openvino",
"mistral",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:43:52+00:00 |
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": []} | golf2248/wtqfpaj | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:45:59+00:00 |
null | null | {} | Cesco2004/TEST.V4 | null | [
"region:us"
] | null | 2024-05-03T13:46:16+00:00 |
|
null | null | {} | solidrinx/test-sloth-24000 | null | [
"gguf",
"region:us"
] | null | 2024-05-03T13:46:37+00:00 |
|
text-generation | transformers | {} | kshitij230/Llama-2-7b-chat-finetune | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:47:29+00:00 |
|
null | null | {} | IkariDev/mememergev1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-03T13:49:50+00:00 |
|
null | null | {} | doodlecodes/test0.02 | null | [
"region:us"
] | null | 2024-05-03T13:51:24+00:00 |
|
text-generation | transformers |
# NeuralMillama-3-8B-MS
NeuralMillama-3-8B-MS is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
## 🧩 Configuration
```yaml
models:
- model: Kukedlc/NeuralMiLLaMa-8B-slerp
- model: Kukedlc/SmartLlama-3-8B-MS-v0.1
- model: mlabonne/ChimeraLlama-3-8B-v2
- model: mlabonne/ChimeraLlama-3-8B-v3
merge_method: model_stock
base_model: mlabonne/ChimeraLlama-3-8B-v3
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuralMillama-3-8B-MS"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit"]} | Kukedlc/NeuralMillama-3-8B-MS | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T13:51:40+00:00 |
feature-extraction | sentence-transformers | This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case:
This model is designed to support various applications in natural language processing and understanding.
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from transformers import AutoModel, AutoTokenizer
llm_name = "jina-embeddings-v2-base-en-03052024-73xx-webapp"
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModel.from_pretrained(llm_name, trust_remote_code=True)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "Events", "Meetups", "Networking", "Community", "Social"], "datasets": ["fine-tuned/jina-embeddings-v2-base-en-03052024-73xx-webapp", "allenai/c4"], "pipeline_tag": "feature-extraction"} | fine-tuned/jina-embeddings-v2-base-en-03052024-73xx-webapp | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Events",
"Meetups",
"Networking",
"Community",
"Social",
"custom_code",
"en",
"dataset:fine-tuned/jina-embeddings-v2-base-en-03052024-73xx-webapp",
"dataset:allenai/c4",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:52:22+00:00 |
null | null | {} | HarryLHT/ChampDatas | null | [
"region:us"
] | null | 2024-05-03T13:52:40+00:00 |
|
null | null | {"license": "other", "license_name": "cwp", "license_link": "LICENSE"} | Sam09384/seikai | null | [
"license:other",
"region:us"
] | null | 2024-05-03T13:52:52+00:00 |
|
null | null | {} | domenicrosati/mxbai-rerank-xsmall-v1 | null | [
"region:us"
] | null | 2024-05-03T13:55:11+00:00 |
|
null | transformers | {} | gdurkin/lulc_v7 | null | [
"transformers",
"safetensors",
"segformer",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:55:29+00:00 |
|
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. -->
# Llama3_instruct_on_charttotext_server
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None 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: 1e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 30
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Llama3_instruct_on_charttotext_server", "results": []}]} | moetezsa/Llama3_instruct_on_charttotext_server | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-05-03T13:56:58+00:00 |
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