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text-generation | transformers |
# Model Card for Model ID
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## 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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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/4c35i9z | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:06:39+00:00 |
null | transformers | {} | notresort/loubie_demo | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:06:56+00:00 |
|
null | null | {} | LightChen2333/deberta-v3-dpf-taap | null | [
"region:us"
]
| null | 2024-04-29T06:07:25+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]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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. -->
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#### Summary
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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]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/4v5ir12 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:08:33+00:00 |
null | null | {} | automated-finetunning/bart_test_22 | null | [
"region:us"
]
| null | 2024-04-29T06:09:05+00:00 |
|
null | null | {"license": "apache-2.0"} | hereankit01/ForcEye | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-29T06:12:19+00:00 |
|
text-generation | null |
# Llama-3-8B-Lexi-Uncensored-GGUF
- This is quantized version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) created using llama.cpp
# Model Description
This model is based on Llama-3-8b-Instruct, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones.
You are responsible for any content you create using this model. Please use it responsibly.
Lexi is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. | {"license": "llama3", "tags": ["uncensored", "llama3", "instruct", "open"], "pipeline_tag": "text-generation", "base_model": "Orenguteng/Llama-3-8B-Lexi-Uncensored"} | QuantFactory/Llama-3-8B-Lexi-Uncensored-GGUF | null | [
"gguf",
"uncensored",
"llama3",
"instruct",
"open",
"text-generation",
"base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"license:llama3",
"region:us"
]
| null | 2024-04-29T06:13:55+00:00 |
null | null | {} | jkushwaha/rough_work | null | [
"region:us"
]
| null | 2024-04-29T06:13:58+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. -->
# zephyr-7b-dpo-full
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5003
- Rewards/chosen: -1.2148
- Rewards/rejected: -2.1807
- Rewards/accuracies: 0.7344
- Rewards/margins: 0.9659
- Logps/rejected: -475.4258
- Logps/chosen: -378.5237
- Logits/rejected: -2.1663
- Logits/chosen: -2.2083
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5746 | 0.21 | 100 | 0.5805 | -0.4531 | -0.9647 | 0.7383 | 0.5117 | -353.8260 | -302.3450 | -2.6379 | -2.6520 |
| 0.5279 | 0.42 | 200 | 0.5292 | -1.1789 | -1.9577 | 0.7539 | 0.7787 | -453.1222 | -374.9346 | -2.3051 | -2.3285 |
| 0.5231 | 0.63 | 300 | 0.5073 | -1.2617 | -2.1547 | 0.7461 | 0.8931 | -472.8262 | -383.2051 | -2.2024 | -2.2375 |
| 0.5069 | 0.84 | 400 | 0.5003 | -1.2148 | -2.1807 | 0.7344 | 0.9659 | -475.4258 | -378.5237 | -2.1663 | -2.2083 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "zephyr-7b-dpo-full", "results": []}]} | wzhouad/zephyr-7b-dpo-full | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:14:10+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. -->
# swin-tiny-patch4-window7-224-finetuned-HOMUS
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1539
- Accuracy: 0.9561
- Precision: 0.9565
- Recall: 0.9561
- F1 Score: 0.9561
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.2499 | 1.0 | 83 | 0.8461 | 0.7127 | 0.7385 | 0.7127 | 0.6973 |
| 0.3816 | 1.99 | 166 | 0.3134 | 0.8838 | 0.8923 | 0.8838 | 0.8802 |
| 0.2474 | 2.99 | 249 | 0.2274 | 0.9184 | 0.9247 | 0.9184 | 0.9176 |
| 0.1276 | 4.0 | 333 | 0.2281 | 0.9197 | 0.9255 | 0.9197 | 0.9194 |
| 0.0933 | 5.0 | 416 | 0.1916 | 0.9364 | 0.9407 | 0.9364 | 0.9361 |
| 0.0629 | 5.99 | 499 | 0.1796 | 0.9443 | 0.9466 | 0.9443 | 0.9441 |
| 0.054 | 6.99 | 582 | 0.1701 | 0.9465 | 0.9466 | 0.9465 | 0.9462 |
| 0.0228 | 8.0 | 666 | 0.1652 | 0.9482 | 0.9484 | 0.9482 | 0.9480 |
| 0.0297 | 9.0 | 749 | 0.1518 | 0.9548 | 0.9552 | 0.9548 | 0.9547 |
| 0.0221 | 9.97 | 830 | 0.1539 | 0.9561 | 0.9565 | 0.9561 | 0.9561 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-HOMUS", "results": []}]} | nadimkanazi/swin-tiny-patch4-window7-224-finetuned-HOMUS | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:14:23+00:00 |
null | null | {} | LightChen2333/deberta-v3-dpf-tsap | null | [
"region:us"
]
| null | 2024-04-29T06:16:14+00:00 |
|
null | null | {} | SatSDev/testingmodel5434523 | null | [
"region:us"
]
| null | 2024-04-29T06:16:17+00:00 |
|
text-to-audio | transformers | {} | madhabpaul/mms-tts-asm-female-1000 | null | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:19:11+00:00 |
|
automatic-speech-recognition | transformers | {} | wadhwani-ai/orf_gu_wav2vec2_mobile_v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:20:38+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": []} | shallow6414/gefxi0j | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:20:53+00:00 |
text-to-image | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of Abid Ali Awan wearing casual clothes, taking a selfie, and smiling.", "inference": true} | sonia456/sdxl-lora | null | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| null | 2024-04-29T06:20:55+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. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [aisingapore/sea-lion-7b](https://huggingface.co/aisingapore/sea-lion-7b) 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "aisingapore/sea-lion-7b", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]} | SuratanBoonpong/code-llama-7b-text-to-sql | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:aisingapore/sea-lion-7b",
"license:mit",
"region:us"
]
| null | 2024-04-29T06:20:56+00:00 |
text-to-image | diffusers |
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout1
<Gallery />
## Model description
### These are aarashfeizi/jean-francois-godbout1 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1.safetensors` here 💾](/aarashfeizi/jean-francois-godbout1/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout1/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1_emb` to your prompt. For example, `A photo of Jean-Francois Godbout`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout1', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout1', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout1_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of Jean-Francois Godbout talking with Joe Biden').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/aarashfeizi/jean-francois-godbout1/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_0.png"}}, {"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_1.png"}}, {"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_2.png"}}, {"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of Jean-Francois Godbout"} | aarashfeizi/jean-francois-godbout1 | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"diffusers-training",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| null | 2024-04-29T06:22:22+00:00 |
automatic-speech-recognition | transformers | {} | wadhwani-ai/orf_hi_wav2vec2_mobile_v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:22:23+00:00 |
|
null | null | {} | CenturionHeart/PIT | null | [
"region:us"
]
| null | 2024-04-29T06:22:47+00:00 |
|
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi01_LoRA
<Gallery />
## Model description
These are embracellm/sushi01_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Albacore Crunch Roll to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi01_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Albacore Crunch Roll", "widget": []} | embracellm/sushi01_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| null | 2024-04-29T06:24:06+00:00 |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# O0428HMA1
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0540
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8099 | 0.09 | 10 | 0.1912 |
| 0.1798 | 0.18 | 20 | 0.1531 |
| 0.1494 | 0.27 | 30 | 0.1613 |
| 0.1557 | 0.36 | 40 | 0.1576 |
| 0.1505 | 0.45 | 50 | 0.1489 |
| 0.1502 | 0.54 | 60 | 0.1467 |
| 0.1486 | 0.63 | 70 | 0.1468 |
| 0.1478 | 0.73 | 80 | 0.1530 |
| 0.1418 | 0.82 | 90 | 0.1254 |
| 0.1393 | 0.91 | 100 | 0.1264 |
| 0.114 | 1.0 | 110 | 0.0868 |
| 0.0713 | 1.09 | 120 | 0.0721 |
| 0.0753 | 1.18 | 130 | 0.1096 |
| 0.0868 | 1.27 | 140 | 0.0649 |
| 0.124 | 1.36 | 150 | 0.0621 |
| 0.058 | 1.45 | 160 | 0.0572 |
| 0.0688 | 1.54 | 170 | 0.0600 |
| 0.0626 | 1.63 | 180 | 0.0618 |
| 0.0673 | 1.72 | 190 | 0.0575 |
| 0.0579 | 1.81 | 200 | 0.0574 |
| 0.0592 | 1.9 | 210 | 0.0554 |
| 0.0577 | 1.99 | 220 | 0.0546 |
| 0.0568 | 2.08 | 230 | 0.0548 |
| 0.0807 | 2.18 | 240 | 0.0912 |
| 0.0728 | 2.27 | 250 | 0.0610 |
| 0.0629 | 2.36 | 260 | 0.0589 |
| 0.0554 | 2.45 | 270 | 0.0552 |
| 0.0523 | 2.54 | 280 | 0.0547 |
| 0.0544 | 2.63 | 290 | 0.0560 |
| 0.0551 | 2.72 | 300 | 0.0541 |
| 0.056 | 2.81 | 310 | 0.0539 |
| 0.0574 | 2.9 | 320 | 0.0540 |
| 0.0586 | 2.99 | 330 | 0.0540 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0428HMA1", "results": []}]} | Litzy619/O0428HMA1 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-29T06:24:48+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": []} | kyounghyun/eeve-levware-kor | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-29T06:25:11+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Starxx
- **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": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Starxx/LLaMa3-Fine-Tuning-ChineseLaw | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:25:45+00:00 |
null | null | {} | automated-finetunning/bart_test_24 | null | [
"region:us"
]
| null | 2024-04-29T06:26:01+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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: mistral/new_data2.jsonl
type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out_mistral_long
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 100
xformers_attention:
flash_attention: false
warmup_steps: 10
evals_per_epoch: 100
eval_table_size:
eval_max_new_tokens: 8192
saves_per_epoch: 10
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# out_mistral_long
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9083
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.0 | 1 | 2.5959 |
| No log | 0.01 | 75 | 1.6174 |
| 2.4248 | 0.02 | 150 | 1.3773 |
| 1.5737 | 0.03 | 225 | 1.2861 |
| 1.4194 | 0.04 | 300 | 1.2337 |
| 1.4194 | 0.05 | 375 | 1.1980 |
| 1.3469 | 0.06 | 450 | 1.1715 |
| 1.2838 | 0.07 | 525 | 1.1483 |
| 1.2454 | 0.08 | 600 | 1.1303 |
| 1.2454 | 0.09 | 675 | 1.1152 |
| 1.2038 | 0.1 | 750 | 1.1021 |
| 1.1923 | 0.11 | 825 | 1.0920 |
| 1.178 | 0.12 | 900 | 1.0829 |
| 1.178 | 0.13 | 975 | 1.0723 |
| 1.1541 | 0.14 | 1050 | 1.0655 |
| 1.1307 | 0.15 | 1125 | 1.0576 |
| 1.1236 | 0.16 | 1200 | 1.0554 |
| 1.1236 | 0.17 | 1275 | 1.0467 |
| 1.1195 | 0.18 | 1350 | 1.0385 |
| 1.1107 | 0.19 | 1425 | 1.0322 |
| 1.1017 | 0.2 | 1500 | 1.0276 |
| 1.1017 | 0.21 | 1575 | 1.0237 |
| 1.0983 | 0.22 | 1650 | 1.0195 |
| 1.0908 | 0.23 | 1725 | 1.0154 |
| 1.0769 | 0.24 | 1800 | 1.0116 |
| 1.0769 | 0.25 | 1875 | 1.0083 |
| 1.068 | 0.26 | 1950 | 1.0029 |
| 1.0711 | 0.27 | 2025 | 0.9990 |
| 1.0693 | 0.28 | 2100 | 0.9956 |
| 1.0693 | 0.29 | 2175 | 0.9925 |
| 1.0595 | 0.3 | 2250 | 0.9903 |
| 1.0633 | 0.31 | 2325 | 0.9870 |
| 1.0547 | 0.32 | 2400 | 0.9830 |
| 1.0547 | 0.33 | 2475 | 0.9807 |
| 1.0489 | 0.34 | 2550 | 0.9778 |
| 1.0373 | 0.35 | 2625 | 0.9759 |
| 1.0494 | 0.36 | 2700 | 0.9724 |
| 1.0494 | 0.37 | 2775 | 0.9704 |
| 1.0413 | 0.38 | 2850 | 0.9682 |
| 1.0304 | 0.39 | 2925 | 0.9649 |
| 1.0263 | 0.4 | 3000 | 0.9630 |
| 1.0263 | 0.41 | 3075 | 0.9612 |
| 1.0181 | 0.42 | 3150 | 0.9583 |
| 1.018 | 0.43 | 3225 | 0.9574 |
| 1.0138 | 0.44 | 3300 | 0.9552 |
| 1.0138 | 0.45 | 3375 | 0.9529 |
| 1.0239 | 0.46 | 3450 | 0.9510 |
| 1.0068 | 0.47 | 3525 | 0.9492 |
| 1.0132 | 0.48 | 3600 | 0.9471 |
| 1.0132 | 0.49 | 3675 | 0.9460 |
| 1.0001 | 0.5 | 3750 | 0.9437 |
| 0.997 | 0.51 | 3825 | 0.9422 |
| 1.0021 | 0.52 | 3900 | 0.9404 |
| 1.0021 | 0.53 | 3975 | 0.9388 |
| 0.9969 | 0.54 | 4050 | 0.9374 |
| 0.995 | 0.55 | 4125 | 0.9362 |
| 0.9975 | 0.56 | 4200 | 0.9347 |
| 0.9975 | 0.57 | 4275 | 0.9333 |
| 0.9881 | 0.58 | 4350 | 0.9324 |
| 0.9954 | 0.59 | 4425 | 0.9307 |
| 0.9963 | 0.6 | 4500 | 0.9297 |
| 0.9963 | 0.61 | 4575 | 0.9286 |
| 0.9908 | 0.62 | 4650 | 0.9273 |
| 0.9853 | 0.63 | 4725 | 0.9264 |
| 0.9842 | 0.64 | 4800 | 0.9251 |
| 0.9842 | 0.65 | 4875 | 0.9244 |
| 0.9878 | 0.66 | 4950 | 0.9231 |
| 0.9838 | 0.67 | 5025 | 0.9221 |
| 0.9791 | 0.68 | 5100 | 0.9214 |
| 0.9791 | 0.69 | 5175 | 0.9204 |
| 0.9772 | 0.7 | 5250 | 0.9193 |
| 0.9771 | 0.71 | 5325 | 0.9185 |
| 0.9708 | 0.72 | 5400 | 0.9177 |
| 0.9708 | 0.73 | 5475 | 0.9170 |
| 0.9829 | 0.74 | 5550 | 0.9161 |
| 0.9738 | 0.75 | 5625 | 0.9155 |
| 0.9698 | 0.76 | 5700 | 0.9148 |
| 0.9698 | 0.77 | 5775 | 0.9140 |
| 0.9726 | 0.78 | 5850 | 0.9133 |
| 0.9707 | 0.79 | 5925 | 0.9130 |
| 0.9734 | 0.8 | 6000 | 0.9124 |
| 0.9734 | 0.81 | 6075 | 0.9120 |
| 0.9701 | 0.82 | 6150 | 0.9114 |
| 0.9703 | 0.83 | 6225 | 0.9110 |
| 0.9764 | 0.84 | 6300 | 0.9107 |
| 0.9764 | 0.85 | 6375 | 0.9103 |
| 0.9707 | 0.86 | 6450 | 0.9099 |
| 0.9765 | 0.88 | 6525 | 0.9097 |
| 0.9776 | 0.89 | 6600 | 0.9094 |
| 0.9776 | 0.9 | 6675 | 0.9092 |
| 0.9675 | 0.91 | 6750 | 0.9090 |
| 0.9585 | 0.92 | 6825 | 0.9088 |
| 0.9651 | 0.93 | 6900 | 0.9087 |
| 0.9651 | 0.94 | 6975 | 0.9086 |
| 0.9638 | 0.95 | 7050 | 0.9085 |
| 0.9693 | 0.96 | 7125 | 0.9084 |
| 0.9659 | 0.97 | 7200 | 0.9084 |
| 0.9659 | 0.98 | 7275 | 0.9083 |
| 0.9626 | 0.99 | 7350 | 0.9083 |
| 0.9643 | 1.0 | 7425 | 0.9083 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "out_mistral_long", "results": []}]} | UlutSoftLLC/chky | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:26:08+00:00 |
text-generation | transformers | {"license": "mit"} | ASHWINKUMARBR/HCD-FASHION-AI | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:26:09+00:00 |
|
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Jackline/HES_Constrastive | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:27:52+00:00 |
text-generation | transformers |
# Model Card for Model ID
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- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/c4o7wwn | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:29:15+00:00 |
null | transformers | {} | LightChen2333/deberta-v3-dpf-mix-atis | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:29:24+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: Joalbom14/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"]} | Joalbom14/poca-SoccerTwos | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| null | 2024-04-29T06:30:38+00:00 |
null | null | {} | CenturionHeart/PHP | null | [
"region:us"
]
| null | 2024-04-29T06:30:41+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[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
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/a49h813 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:33:24+00:00 |
null | null | {} | saimdev/custom_urdu_tts_model_refined_0 | null | [
"region:us"
]
| null | 2024-04-29T06:33:57+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. -->
# selfbiorag-7b-dpo-full-sft-wo-live_qa
This model is a fine-tuned version of [Minbyul/selfbiorag-7b-wo-live_qa-sft](https://huggingface.co/Minbyul/selfbiorag-7b-wo-live_qa-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1422
- Rewards/chosen: -1.2709
- Rewards/rejected: -13.2633
- Rewards/accuracies: 0.9167
- Rewards/margins: 11.9924
- Logps/rejected: -1991.3534
- Logps/chosen: -456.8682
- Logits/rejected: -0.4049
- Logits/chosen: -0.4878
## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.2635 | 0.3 | 100 | 0.1990 | -0.5114 | -9.8179 | 0.9167 | 9.3065 | -1646.8138 | -380.9204 | -0.1085 | -0.3091 |
| 0.1415 | 0.61 | 200 | 0.1502 | -0.9081 | -11.0651 | 0.9167 | 10.1570 | -1771.5302 | -420.5836 | -0.4280 | -0.4824 |
| 0.0892 | 0.91 | 300 | 0.1421 | -1.2604 | -13.2286 | 0.9167 | 11.9683 | -1987.8828 | -455.8129 | -0.4048 | -0.4887 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/selfbiorag-7b-wo-live_qa-sft", "model-index": [{"name": "selfbiorag-7b-dpo-full-sft-wo-live_qa", "results": []}]} | Minbyul/selfbiorag-7b-dpo-full-sft-wo-live_qa | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Minbyul/selfbiorag-7b-wo-live_qa-sft",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:36:43+00:00 |
null | transformers | {} | LightChen2333/deberta-v3-dpf-mix-snips | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:37:40+00:00 |
|
text-classification | transformers | {} | niksyy/my_awesome_model | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:39:15+00:00 |
|
null | null | {} | vorstcavry/Sony-XZ2-XZ3 | null | [
"region:us"
]
| null | 2024-04-29T06:39:19+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]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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
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#### 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
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lunarsylph/stablecell_v49 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:40:29+00:00 |
null | null | {} | automated-finetunning/bart_test_26 | null | [
"region:us"
]
| null | 2024-04-29T06:40:46+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.
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[More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Crysiss/llama-3-8B-healthcare-15000-diff-epoch | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:41:42+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. -->
# grad_ascent_2e-05_WikiMIA_QA_256_10
This model is a fine-tuned version of [openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 26
- training_steps: 267
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "openlm-research/open_llama_7b", "model-index": [{"name": "grad_ascent_2e-05_WikiMIA_QA_256_10", "results": []}]} | lluvecwonv/grad_ascent_2e-05_WikiMIA_QA_256_10 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openlm-research/open_llama_7b",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-29T06:41:46+00:00 |
automatic-speech-recognition | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **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
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[More Information Needed]
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[More Information Needed]
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[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]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | devkya/uandi-whipser-peft | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:42:04+00:00 |
text-to-image | diffusers | # sdxl
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/sonia456/sdxl/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "generate a background for the given image product", "parameters": {"negative_prompt": "do not change the input image"}, "output": {"url": "images/Frame 38.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0"} | sonia456/sdxl | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| null | 2024-04-29T06:43:17+00:00 |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-LastProvaClust-Cluster1di4-5Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-LastProvaClust-Cluster1di4-5Epochs", "results": []}]} | tedad09/PolizzeDonut-LastProvaClust-Cluster1di4-5Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:43:18+00:00 |
text-generation | transformers |
<p align="center" style="margin:0;padding:0">
<img src="https://huggingface.co/BramVanroy/fietje-2b-chat/resolve/main/img/fietje-2b-banner-rounded.png" alt="Fietje banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>
<div style="margin:auto; text-align:center">
<h1 style="margin-bottom: 0">Fietje 2B Chat</h1>
<em>An open and efficient LLM for Dutch</em>
</div>
<blockquote class="tip" style="padding: 1.5em; border: 0">
<p align="center" style="text-align: center; margin: 0">
<a href="https://huggingface.co/BramVanroy/fietje-2b">👱♀️ Base version</a> -
<a href="https://huggingface.co/BramVanroy/fietje-2b-instruct">🤖 Instruct version</a> -
<a href="https://huggingface.co/BramVanroy/fietje-2b-chat">💬 Chat version</a> (this one) -
<a href="https://huggingface.co/BramVanroy/fietje-2b-chat-GGUF">🚀 GGUF of Chat</a>
</p>
<p align="center" style="text-align: center; margin: 0">
<a href="https://huggingface.co/spaces/BramVanroy/fietje-2b"><strong>Chat with Fietje here!</strong></a>
</p>
</blockquote>
This is the chat version of Fietje, a DPO-tuned (aligned) continuation on [the instruct version](https://huggingface.co/BramVanroy/fietje-2b-instruct). Fietje is an adapated version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like [GEITje 7B Ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra).
A thorough description of the creation and evaluation of Fietje as well as usage examples are available in [this Github repository](https://github.com/BramVanroy/fietje).
## Intended uses & limitations
The same limitations as [phi-2](https://huggingface.co/microsoft/phi-2#limitations-of-phi-2), and LLMs in general, apply here. LLMs hallucinate, make mistakes, and should not be trusted. Use at your own risk!
## Training and evaluation data
Fietje 2B instruct was finetuned from [the instruct model](https://huggingface.co/BramVanroy/fietje-2b-instruct) on the following datasets. Number of training samples per dataset given in brackets, totalling 18,653 samples.
- [BramVanroy/ultra_feedback_dutch_cleaned](https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch_cleaned) subset `dpo_hq`: a cleaned version of [BramVanroy/ultra_feedback_dutch](https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch) (9186)
- [BramVanroy/orca_dpo_pairs_dutch_cleaned](https://huggingface.co/datasets/BramVanroy/orca_dpo_pairs_dutch_cleaned) subset `dpo_all`: a cleaned version of [BramVanroy/orca_dpo_pairs_dutch](https://huggingface.co/datasets/BramVanroy/orca_dpo_pairs_dutch) (9467)
A lot of different learning rates, beta, en batch sizes were investigated in search of a converging combination. You can find them all in [the W&B runs](https://wandb.ai/bramvanroy/dpo-fietje-2b).
## Training procedure
I am thankful to the [Flemish Supercomputer Center](https://www.vscentrum.be/) (VSC) for providing the computational power to accomplish this project. Accounting for waiting for jobs, training a single run took around nine hours on one A100 80GB.
Training was done with the wonderful [alignment-handbook](https://github.com/huggingface/alignment-handbook), using DeepSpeed as a back-end. Exact training recipes and SLURM script are given in the [Github repository](https://github.com/BramVanroy/fietje).
### Training hyperparameters
The following hyperparameters were used during training:
- beta: 0.2
- learning_rate: 2e-06
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.2515 | 1.0 | 1166 | 0.2842 | -1.1549 | -3.6363 | 0.8867 | 2.4815 | -657.6813 | -451.3364 | -1.2868 | -1.3528 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"language": ["nl"], "license": "mit", "tags": ["trl", "fietje", "alignment-handbook", "dpo"], "datasets": ["BramVanroy/ultra_feedback_dutch_cleaned", "BramVanroy/orca_dpo_pairs_dutch_cleaned"], "base_model": "BramVanroy/fietje-2b-instruct", "pipeline_tag": "text-generation", "inference": false, "model-index": [{"name": "fietje-2b-chat", "results": []}]} | BramVanroy/fietje-2b-chat | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"trl",
"fietje",
"alignment-handbook",
"dpo",
"conversational",
"nl",
"dataset:BramVanroy/ultra_feedback_dutch_cleaned",
"dataset:BramVanroy/orca_dpo_pairs_dutch_cleaned",
"base_model:BramVanroy/fietje-2b-instruct",
"license:mit",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:43:38+00:00 |
null | null | {} | Bhawana/gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-29T06:44:07+00:00 |
|
null | null |
Check out the configuration reference at https://huggingface.co/spaces/harshinde/AI-web-app-with-OpenAI-CLIP-model
pip install -r requirements.txt | {"language": ["en"], "license": "mit", "title": "AI Web App With OpenAI CLIP Model", "emoji": "\ud83d\udcc9", "colorFrom": "green", "colorTo": "red", "sdk": "streamlit", "sdk_version": "1.33.0", "app_file": "app.py", "pinned": false} | harshinde/AI-web-app-with-OpenAI-CLIP-model | null | [
"en",
"license:mit",
"region:us"
]
| null | 2024-04-29T06:44:38+00:00 |
null | null | {"license": "mit"} | dennisvdang/chorus-detection | null | [
"license:mit",
"region:us"
]
| null | 2024-04-29T06:44:49+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]
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## Uses
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | cody628/bloomz-560m_PROMPT_TUNING_CAUSAL_LM | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:45:09+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.
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/b00ggju | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:47:13+00:00 |
text-to-audio | 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. -->
# zlm_b64_le4_s12000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3114
## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 12000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.5487 | 0.4188 | 500 | 0.4746 |
| 0.483 | 0.8375 | 1000 | 0.4227 |
| 0.432 | 1.2563 | 1500 | 0.3983 |
| 0.429 | 1.6750 | 2000 | 0.3953 |
| 0.4168 | 2.0938 | 2500 | 0.3701 |
| 0.4021 | 2.5126 | 3000 | 0.3613 |
| 0.3925 | 2.9313 | 3500 | 0.3509 |
| 0.3839 | 3.3501 | 4000 | 0.3506 |
| 0.3798 | 3.7688 | 4500 | 0.3423 |
| 0.3693 | 4.1876 | 5000 | 0.3375 |
| 0.3712 | 4.6064 | 5500 | 0.3367 |
| 0.3668 | 5.0251 | 6000 | 0.3316 |
| 0.3635 | 5.4439 | 6500 | 0.3291 |
| 0.3543 | 5.8626 | 7000 | 0.3250 |
| 0.3526 | 6.2814 | 7500 | 0.3221 |
| 0.3525 | 6.7002 | 8000 | 0.3218 |
| 0.3513 | 7.1189 | 8500 | 0.3182 |
| 0.346 | 7.5377 | 9000 | 0.3163 |
| 0.3448 | 7.9564 | 9500 | 0.3162 |
| 0.3563 | 8.3752 | 10000 | 0.3145 |
| 0.3449 | 8.7940 | 10500 | 0.3126 |
| 0.3436 | 9.2127 | 11000 | 0.3128 |
| 0.3413 | 9.6315 | 11500 | 0.3121 |
| 0.3397 | 10.0503 | 12000 | 0.3114 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "zlm_b64_le4_s12000", "results": []}]} | mikhail-panzo/zlm_b64_le4_s12000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:47:22+00:00 |
null | null | {} | AnaNoSleep/archieves | null | [
"region:us"
]
| null | 2024-04-29T06:47:40+00:00 |
|
null | null |
# DavidAU/LlamaGramma-7b-Q8_0-GGUF
This model was converted to GGUF format from [`Gryphe/LlamaGramma-7b`](https://huggingface.co/Gryphe/LlamaGramma-7b) 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/Gryphe/LlamaGramma-7b) 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 DavidAU/LlamaGramma-7b-Q8_0-GGUF --model llamagramma-7b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LlamaGramma-7b-Q8_0-GGUF --model llamagramma-7b.Q8_0.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 llamagramma-7b.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "other", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["Gryphe/CoEdit-Alpaca"]} | DavidAU/LlamaGramma-7b-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:Gryphe/CoEdit-Alpaca",
"license:other",
"region:us"
]
| null | 2024-04-29T06:48:53+00:00 |
null | null | Apakah Varicap Tablet?
Varicap harga berdiri sebagai kapsul berkualiti premium yang direka untuk membantu dalam menguruskan hipertensi dan menggalakkan kesihatan kardiovaskular. Formula termajunya mengintegrasikan gabungan sinergistik herba, vitamin dan mineral, dipilih dengan teliti untuk menangani punca tekanan darah tinggi.
Laman web rasmi:<a href="https://www.nutritionsee.com/varicoama">www.Varicap.com</a>
<p><a href="https://www.nutritionsee.com/varicoama"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Varicap-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/varicoama">
Beli sekarang!! Klik pautan di bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang... Cepat</a>
Laman web rasmi:<a href="https://www.nutritionsee.com/varicoama">www.Varicap.com</a>
| {"license": "apache-2.0"} | Varicap/Varicap | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-29T06:49:00+00:00 |
null | null | {} | hatakeyama-llm-team/TanukiV0 | null | [
"region:us"
]
| null | 2024-04-29T06:49:08+00:00 |
|
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Nbalaji/flant5-large-AIA | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:52:16+00:00 |
null | null | {} | taeseen/llama-2-7b-chat-kor-oph | null | [
"region:us"
]
| null | 2024-04-29T06:52:21+00:00 |
|
automatic-speech-recognition | transformers |
# whisper-large-v3-ParlaSpeech-HR
This model was trained on 1000k hours of ParlaSpeech-HR, with sampling expressly designed to offer the biggest speaker variance and 50-50 gender composition.
<!--
## 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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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]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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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).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"license": "cc-by-sa-4.0", "library_name": "transformers", "datasets": ["classla/ParlaSpeech-HR"]} | 5roop/whisper-large-v3-ParlaSpeech-HR | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"dataset:classla/ParlaSpeech-HR",
"arxiv:1910.09700",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:52:33+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]
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### 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. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | sm-9967/aphasia-wav2vec2-demo-google-colab | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:52:46+00:00 |
null | null | {} | ivykopal/czech_prompt_cssquad_prompt_100k | null | [
"region:us"
]
| null | 2024-04-29T06:52:49+00:00 |
|
text-to-image | diffusers |
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout2
<Gallery />
## Model description
### These are aarashfeizi/jean-francois-godbout2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2.safetensors` here 💾](/aarashfeizi/jean-francois-godbout2/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout2/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2_emb` to your prompt. For example, `A photo of Jean-Francois Godbout`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout2', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout2', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout2_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of Jean-Francois Godbout giving a speech').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/aarashfeizi/jean-francois-godbout2/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of Jean-Francois Godbout giving a speech", "output": {"url": "image_0.png"}}, {"text": "A photo of Jean-Francois Godbout giving a speech", "output": {"url": "image_1.png"}}, {"text": "A photo of Jean-Francois Godbout giving a speech", "output": {"url": "image_2.png"}}, {"text": "A photo of Jean-Francois Godbout giving a speech", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of Jean-Francois Godbout"} | aarashfeizi/jean-francois-godbout2 | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"diffusers-training",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| null | 2024-04-29T06:53:11+00:00 |
text-generation | null |
<div align="center">
<h1>Llama-3-8B-Instruct-80K-QLoRA</h1>
<a href="https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/longllm_qlora">[Data&Code]</a>
</div>
We extend the context length of Llama-3-8B-Instruct to 80K using QLoRA and 3.5K long-context training data synthesized from GPT-4. The entire training cycle is super efficient, which takes 8 hours on a 8xA800 (80G) machine. Yet, the resulted model achieves remarkable performance on a series of downstream long-context evaluation benchmarks.
# Evaluation
All the following evaluation results can be reproduced following instructions [here](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/longllm_qlora).
## Needle in a Haystack
We evaluate the model on the Needle-In-A-HayStack task using the official setting. The blue vertical line indicates the training context length, i.e. 80K.
<img src="data/needle.png"></img>
## LongBench
We evaluate the model on [LongBench](https://arxiv.org/abs/2308.14508) using 32K context length and the official prompt template. For [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), we use 8K context length.
|Model|Single-Doc QA|Multi-Doc QA|Summarization|Few-Shot Learning|Synthetic|Code|Avg|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|[meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)|37.33|36.04|26.83|**69.56**|37.75|53.24|43.20|
|[gradientai/Llama-3-8B-Instruct-262k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k)|37.29|31.20|26.18|67.25|44.25|**62.71**|43.73|
|[Llama-3-8B-Instruct-80K-QLoRA]()|**43.57**|**43.07**|**28.93**|69.15|**48.50**|51.95|**47.19**|
## InfiniteBench
We evaluate the model on [InfiniteBench](https://arxiv.org/pdf/2402.13718.pdf) using 80K context length and the official prompt template. The results of GPT-4 is copied from the [paper](https://arxiv.org/pdf/2402.13718.pdf). For [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), we use 8K context length.
|Model|LongBookQA Eng|LongBookSum Eng|
|:-:|:-:|:-:|
|GPT-4|22.22|14.73|
|[meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)|7.00|**16.40**|
|[gradientai/Llama-3-8B-Instruct-262k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k)|20.30|10.34|
|[Llama-3-8B-Instruct-80K-QLoRA]()|**30.92**|14.73|
## Topic Retrieval
We evaluate the model on [Topic Retrieval](https://lmsys.org/blog/2023-06-29-longchat/) task with `[5,10,15,20,25,30,40,50,60,70]` topics.
<img src="data/topic.png"></img>
## MMLU
We evaluate the model's zero-shot performance on MMLU benchmark as a reflection of its short-context capability.
|Model|STEM|Social Sciences|Humanities|Others|Avg|
|:-:|:-:|:-:|:-:|:-:|:-:|
|[Llama-2-7B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|35.92|54.37|51.74|51.42|47.22|
|[Mistral-7B-v0.2-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)|48.79|69.95|64.99|61.64|60.10|
|[meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)|**53.87**|**75.66**|**69.44**|69.75|**65.91**|
|[gradientai/Llama-3-8B-Instruct-262k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k)|52.10|73.26|67.15|**69.80**|64.34|
|[Llama-3-8B-Instruct-80K-QLoRA]()|53.10|73.24|67.32|68.79|64.44|
# Environment
```bash
torch==2.2.2
flash_attn==2.5.6
transformers==4.39.3
peft==0.10.0
```
# Usage
```python
import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
peft_id = "namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA"
torch_dtype = torch.bfloat16
# place the model on GPU
device_map = {"": "cuda"}
tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map=device_map,
attn_implementation="flash_attention_2",
# NOTE: expand rope base
rope_theta=200e6,
)
model = PeftModel.from_pretrained(
base_model,
peft_id,
torch_dtype=torch.bfloat16,
device_map=device_map,
)
# NOTE: merge LoRA weights
model = model.merge_and_unload().eval()
with torch.no_grad():
# short context
messages = [{"role": "user", "content": "Tell me about yourself."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50)[:, inputs["input_ids"].shape[1]:]
print(f"Input Length: {inputs['input_ids'].shape[1]}")
print(f"Output: {tokenizer.decode(outputs[0])}")
# long context
with open("data/narrativeqa.json", encoding="utf-8") as f:
example = json.load(f)
messages = [{"role": "user", "content": example["context"]}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
outputs = model.generate(**inputs, do_sample=False, top_p=1, temperature=1, max_new_tokens=20)[:, inputs["input_ids"].shape[1]:]
print("*"*20)
print(f"Input Length: {inputs['input_ids'].shape[1]}")
print(f"Answers: {example['answer']}")
print(f"Prediction: {tokenizer.decode(outputs[0])}")
```
You may observe messages like:
`This is a friendly reminder - the current text generation call will exceed the model's predefined maximum length (8192). Depending on the model, you may observe exceptions, performance degradation, or nothing at all.` or `Setting pad_token_id to eos_token_id:128001 for open-end generation`. They do not matter. Just ignore them. | {"license": "mit", "pipeline_tag": "text-generation"} | namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA | null | [
"safetensors",
"text-generation",
"conversational",
"arxiv:2308.14508",
"arxiv:2402.13718",
"license:mit",
"region:us"
]
| null | 2024-04-29T06:53:31+00:00 |
null | null | {} | mozksoft/test1-mistoonSapphire-v20-coreml-q6 | null | [
"region:us"
]
| null | 2024-04-29T06:53:54+00:00 |
|
null | null | {} | mozksoft/test2-mistoonSapphire-v20-coreml-q6 | null | [
"region:us"
]
| null | 2024-04-29T06:54:10+00:00 |
|
text-generation | transformers | # merged_folder
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Mistral-7B-Instruct-v0.2 as a base.
### Models Merged
The following models were included in the merge:
* D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Kunoichi-DPO-v2-7B
* D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\NeuralOmniWestBeaglake-7B
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Kunoichi-DPO-v2-7B
parameters:
weight: 0.4
- model: D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\NeuralOmniWestBeaglake-7B
parameters:
weight: 0.6
base_model: D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Mistral-7B-Instruct-v0.2
merge_method: task_arithmetic
dtype: bfloat16
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": []} | shyamieee/Maverick-v1.0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:54:34+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.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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### 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **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]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | harir/gemma-combined-test | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:56:36+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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### 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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | hyojin99/wav2vec2-base | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T06:57:28+00:00 |
null | null | {} | mochou/ddpm-butterflies-128 | null | [
"region:us"
]
| null | 2024-04-29T06:58:27+00:00 |
|
null | null | # GGUF of [NeuralNovel/Llama-3-NeuralPaca-8b](https://huggingface.co/NeuralNovel/Llama-3-NeuralPaca-8b)
| {"license": "apache-2.0"} | CultriX/Llama-3-NeuralPaca-8b-GGUF | null | [
"gguf",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-29T06:58:48+00:00 |
text-generation | transformers | tiny Llama trained on BRO dataset with NER tags, Labels and Tokens.
WCETrainer r100_O10_f100 , run lemon-fog-11 checkpoint-1623.
- EVAL
AVGf1 = 93%, overall_f1 = 82%
- TEST
'DIAG': {'precision': 0.710079275198188, 'recall': 0.7674418604651163, 'f1': 0.7376470588235293, 'number': 817},
'MED': {'precision': 0.9379084967320261, 'recall': 0.959866220735786, 'f1': 0.9487603305785124, 'number': 299},
'TREAT': {'precision': 0.8542914171656687, 'recall': 0.856, 'f1': 0.8551448551448552, 'number': 500},
'overall_precision': 0.7672955974842768, 'overall_recall': 0.8304455445544554, 'overall_f1': 0.7976225854383358, 'overall_accuracy': 0.9280119624038735}
average_f1 = 0.8471840815156323
- Prompt Format (see example):
### ### Context\n{Nachricht}\n\n### Answer
def context_text(text):
return f"### Context\n{text}\n\n### Answer" | {"language": "ger", "license": "apache-2.0", "widget": [{"text": "###Context\nDer Patient hat Brustkrebs.\n\n###Answer", "example_title": "Patient mit Brustkreb"}], "pipeline_tag": "text-generation"} | MSey/tiny_BROLLLT_0001.1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ger",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T06:58:52+00:00 |
token-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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0623
- Precision: 0.9339
- Recall: 0.9505
- F1: 0.9421
- Accuracy: 0.9864
## 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0756 | 1.0 | 1756 | 0.0670 | 0.9020 | 0.9360 | 0.9187 | 0.9812 |
| 0.0342 | 2.0 | 3512 | 0.0632 | 0.9311 | 0.9463 | 0.9387 | 0.9860 |
| 0.0198 | 3.0 | 5268 | 0.0623 | 0.9339 | 0.9505 | 0.9421 | 0.9864 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | AFZAL0008/bert-finetuned-ner | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:00:42+00:00 |
null | null | {} | dubm/melotts_v1 | null | [
"region:us"
]
| null | 2024-04-29T07:00:57+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Juniplayground/juniper-mxbai-embed-large-v1-more-data | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:01:12+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)
Hermes-2-Pro-Mistral-7B - bnb 4bits
- Model creator: https://huggingface.co/NousResearch/
- Original model: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B/
Original model description:
---
base_model: mistralai/Mistral-7B-v0.1
tags:
- Mistral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
model-index:
- name: Hermes-2-Pro-Mistral-7B
results: []
license: apache-2.0
language:
- en
datasets:
- teknium/OpenHermes-2.5
widget:
- example_title: Hermes 2 Pro
messages:
- role: system
content: You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
- role: user
content: Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.
---
# Hermes 2 Pro - Mistral 7B
## Model Description
Hermes 2 Pro on Mistral 7B is the new flagship 7B Hermes!
Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.
This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation.
Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below.
This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
## Thank you to Latitude.sh for sponsoring compute for this model!
## Example Outputs
### Explaining Problems with Quantum Gravity:
### Roleplaying as a Cosmic Super Intelligence:
### Detailing the Theory of AI Consciousness in JSON
# Prompt Format
Hermes 2 Pro uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
## Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Benchmarks
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5461|± |0.0145|
| | |acc_norm|0.5623|± |0.0145|
|arc_easy | 0|acc |0.8157|± |0.0080|
| | |acc_norm|0.7934|± |0.0083|
|boolq | 1|acc |0.8688|± |0.0059|
|hellaswag | 0|acc |0.6272|± |0.0048|
| | |acc_norm|0.8057|± |0.0039|
|openbookqa | 0|acc |0.3360|± |0.0211|
| | |acc_norm|0.4300|± |0.0222|
|piqa | 0|acc |0.7954|± |0.0094|
| | |acc_norm|0.7998|± |0.0093|
|winogrande | 0|acc |0.7230|± |0.0126|
```
Average: 71.19
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2047|± |0.0254|
| | |acc_norm|0.2283|± |0.0264|
|agieval_logiqa_en | 0|acc |0.3779|± |0.0190|
| | |acc_norm|0.3932|± |0.0192|
|agieval_lsat_ar | 0|acc |0.2652|± |0.0292|
| | |acc_norm|0.2522|± |0.0287|
|agieval_lsat_lr | 0|acc |0.5216|± |0.0221|
| | |acc_norm|0.5137|± |0.0222|
|agieval_lsat_rc | 0|acc |0.5911|± |0.0300|
| | |acc_norm|0.5836|± |0.0301|
|agieval_sat_en | 0|acc |0.7427|± |0.0305|
| | |acc_norm|0.7184|± |0.0314|
|agieval_sat_en_without_passage| 0|acc |0.4612|± |0.0348|
| | |acc_norm|0.4466|± |0.0347|
|agieval_sat_math | 0|acc |0.3818|± |0.0328|
| | |acc_norm|0.3545|± |0.0323|
```
Average: 44.52
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5579|± |0.0361|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6694|± |0.0245|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3333|± |0.0294|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2061|± |0.0214|
| | |exact_str_match |0.2256|± |0.0221|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3120|± |0.0207|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2114|± |0.0154|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4900|± |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3600|± |0.0215|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6660|± |0.0105|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4420|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2766|± |0.0142|
|bigbench_snarks | 0|multiple_choice_grade|0.6630|± |0.0352|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6653|± |0.0150|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3190|± |0.0147|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2128|± |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1737|± |0.0091|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4900|± |0.0289|
```
Average: 41.65
## TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4100|± |0.0172|
| | |mc2 |0.5911|± |0.0158|
```
# Function Calling Evaluations
We worked with Fireworks.AI on evaluations by starting off with their Function Calling eval dataset, fixing some unsolveable ones, and generating a second eval dataset for JSON mode.
## Function Calling Accuracy: 91%
## JSON Mode Accuracy: 84%
Run the evaluator yourself using @interstellarninja's codebase here:
https://github.com/interstellarninja/function-calling-eval
You can find the evaluation datasets here:
https://huggingface.co/datasets/NousResearch/func-calling-eval
https://huggingface.co/datasets/NousResearch/json-mode-eval
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import LlamaTokenizer, MistralForCausalLM
import bitsandbytes, flash_attn
tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Mistral-7B', trust_remote_code=True)
model = MistralForCausalLM.from_pretrained(
"NousResearch/Hermes-2-Pro-Mistral-7B",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
## Inference Code for Function Calling:
All code for utilizing, parsing, and building function calling templates is available on our github:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
# Chat Interfaces
When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
## Quantized Versions:
GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF
# How to cite:
```bibtext
@misc{Hermes-2-Pro-Mistral-7B,
url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B]https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)},
title={Hermes-2-Pro-Mistral-7B},
author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"}
}
```
| {} | RichardErkhov/NousResearch_-_Hermes-2-Pro-Mistral-7B-4bits | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-29T07:01:15+00:00 |
null | null | {} | jinwoos/shadow_only | null | [
"region:us"
]
| null | 2024-04-29T07:01:19+00:00 |
|
text-generation | transformers | {} | TheoND/testQAver3job5 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T07:01:26+00:00 |
|
text-generation | transformers | {"license": "apache-2.0"} | jayasuryajsk/Mistral-spoken-telugu | null | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T07:04:17+00:00 |
|
text2text-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": []} | Nbalaji/flant5-large-aio | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T07:04:41+00:00 |
null | null | {} | Aditi25/falcon-7b-instruct-HPE | null | [
"region:us"
]
| null | 2024-04-29T07:04:42+00:00 |
|
null | null | {} | cp0987/llama3 | null | [
"region:us"
]
| null | 2024-04-29T07:04:47+00:00 |
|
null | null | {"license": "unknown"} | jensen1207/Qwen-1.8B-q4f16_1-MLC | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-29T07:05:44+00:00 |
|
null | null | {} | CenturionHeart/VENUSBOD | null | [
"region:us"
]
| null | 2024-04-29T07:05:47+00:00 |
|
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r jinghuanHuggingface/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "11.53 +/- 4.01", "name": "mean_reward", "verified": false}]}]}]} | jinghuanHuggingface/rl_course_vizdoom_health_gathering_supreme | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-29T07:05:49+00:00 |
null | null | {} | Ankit15nov/my_awesome_model | null | [
"region:us"
]
| null | 2024-04-29T07:06:10+00:00 |
|
text-classification | transformers | {} | KalaiselvanD/albert_model__29_2 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:06:41+00:00 |
|
text-classification | transformers | {"language": ["en"], "library_name": "transformers", "tags": ["sentiment analysis", "NLP", "BERT", "Transformers"], "datasets": ["zeroshot/twitter-financial-news-sentiment"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | Hossein-NK/twitter-financial-news-sentiment | null | [
"transformers",
"sentiment analysis",
"NLP",
"BERT",
"Transformers",
"text-classification",
"en",
"dataset:zeroshot/twitter-financial-news-sentiment",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:06:41+00:00 |
|
feature-extraction | transformers | {} | fluttersunny/dummy-model | null | [
"transformers",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:06:48+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** chienweichang
- **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": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | chienweichang/Meta-Llama-3-8B-GGUF | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:06:58+00:00 |
question-answering | 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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1602
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2158 | 1.0 | 5533 | 1.1646 |
| 0.9566 | 2.0 | 11066 | 1.1025 |
| 0.7628 | 3.0 | 16599 | 1.1602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Marwan9993/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:07:45+00:00 |
question-answering | 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. -->
# QA_model4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 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
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "QA_model4", "results": []}]} | MattNandavong/QA_model4 | null | [
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:07:53+00:00 |
fill-mask | transformers | {"license": "mit"} | Chaoqi2000/BestDNA6mer | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"custom_code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:08:19+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": []} | ohsuz/exp_dus | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-29T07:08:31+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. -->
# git-base-cancer
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/git-base", "model-index": [{"name": "git-base-cancer", "results": []}]} | arshi229/git-base-cancer | null | [
"transformers",
"tensorboard",
"safetensors",
"git",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/git-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:09:39+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/euclaise/ReMask-3B
# From original readme
## Prompt format
The format for reddit-instruct and oasst2 was:
```
<|user|>
[insert instruction here]
<|assistant|>
[insert response here]
<|user|>
...
```
The format for TinyCoT was:
```
<|user|>
[insert instruction here]
<|rationale|>
[insert reasoning here]
<|answer|>
[insert direct answer here]
``` | {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "ReMask-3B"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/ReMask-3B-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"ReMask-3B",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-29T07:09:58+00:00 |
null | null |
# Ognoexperiment27multi_verse_modelYamshadowexperiment28-7B
Ognoexperiment27multi_verse_modelYamshadowexperiment28-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: automerger/Ognoexperiment27Multi_verse_model-7B
- model: automerger/YamshadowExperiment28-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Ognoexperiment27multi_verse_modelYamshadowexperiment28-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/Ognoexperiment27multi_verse_modelYamshadowexperiment28-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-29T07:10:30+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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## 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. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]} | nayan8625/llama3-8b-ymhafinetuned | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-29T07:12:30+00:00 |
text-to-image | diffusers |
# 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 🧨 diffusers 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. -->
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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": "diffusers"} | rubbrband/slatePencilMix_v10 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| null | 2024-04-29T07:15:02+00:00 |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PolizzeDonut-LastProvaClust-Cluster2di4-5Epochs
This model is a fine-tuned version of [tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0](https://huggingface.co/tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0) on the imagefolder 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0", "model-index": [{"name": "PolizzeDonut-LastProvaClust-Cluster2di4-5Epochs", "results": []}]} | tedad09/PolizzeDonut-LastProvaClust-Cluster2di4-5Epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:tedad09/PolizzeDonut-ChangeRequest-imm5epochs-Expand0",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-29T07:15:36+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - checodo/alma-1.0
<Gallery />
## Model description
These are checodo/alma-1.0 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use "TOK map" to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](checodo/alma-1.0/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "\"TOK map\"", "widget": []} | checodo/alma-1.0 | null | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| null | 2024-04-29T07:17:15+00:00 |
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