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almersawi/test-tinyllama-1 | almersawi | 2024-03-19T19:34:07Z | 89 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"openinnovatioai-mlops",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-19T19:33:17Z | ---
library_name: transformers
tags:
- gpt
- llm
- large language model
- openinnovatioai-mlops
inference: false
---
# Model Card
This model was trained using [OpenInnovationAI MLOps](https://openinnovation.ai/products/open-innovation-ai-os-transforming-enterprise-ai-and-mlops).
- Base model: [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) |
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_64_64_0.01_4_0.0002 | ferrazzipietro | 2024-03-19T19:30:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T19:29:33Z | ---
library_name: transformers
tags: []
---
# 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
cerebras/Cerebras-ViT-L-336-patch14-llava13b-ShareGPT4V | cerebras | 2024-03-19T19:27:49Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"clip_vision_model",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T19:13:00Z | ---
{}
---
# Model Card for Cerebras-ViT-L-336-patch14-llava13b-ShareGPT4V
The checkpoints here are for the vision encoder part of [**cerebras/Cerebras-LLaVA-13B**](https://huggingface.co/cerebras/Cerebras-LLaVA-13B).
**Note**: _ShareGPT4V_ is added to the model name to ensure correct loading of checkpoints in [LLaVA source repo](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/builder.py#L8)
For full details of this model and training details, please read our upcoming blog post.
## License:
Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Model Architecture
Cerebras-ViT-L-336-patch14-llava13b-ShareGPT4V is a transformer model based on CLIP-VisionModel-Large(openai/clip-vit-large-patch14-336). It handles images of size 336 x 336 with patch size of 14
## Intended Use
_Primary intended uses_: The primary use of LLaVA is research on large multimodal models and chatbots.
_Primary intended users_: The primary intended users of the model are researchers(both academic and industry) in computer vision, natural language processing, machine learning, and artificial intelligence
## Limitations and Bias
The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models for their applications or any use case that may cause deliberate or unintentional harm to others. This model is for demonstration purpose only.
|
dagbs/quietstar-8-ahead-GGUF | dagbs | 2024-03-19T19:23:53Z | 38 | 5 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T18:06:59Z | # quietstar-8-ahead - GGUF
Original Model: [ezelikman/quietstar-8-ahead](https://huggingface.co/ezelikman/quietstar-8-ahead) |
prajwalJumde/rap_phase2_19march_5i_v1 | prajwalJumde | 2024-03-19T19:23:40Z | 110 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-03-19T13:11:16Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: rap_phase2_19march_5i_v1
results: []
---
<!-- 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. -->
# rap_phase2_19march_5i_v1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0179
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 21
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1239 | 1.0 | 2505 | 0.0716 |
| 0.0849 | 2.0 | 5010 | 0.0479 |
| 0.0331 | 3.0 | 7515 | 0.1480 |
| 0.025 | 4.0 | 10020 | 0.0318 |
| 0.0828 | 5.0 | 12525 | 0.0325 |
| 0.0533 | 6.0 | 15030 | 0.0291 |
| 0.0163 | 7.0 | 17535 | 0.0336 |
| 0.0128 | 8.0 | 20040 | 0.0213 |
| 0.0124 | 9.0 | 22545 | 0.0179 |
| 0.0118 | 10.0 | 25050 | 0.0228 |
| 0.0028 | 11.0 | 27555 | 0.0130 |
| 0.0056 | 12.0 | 30060 | 0.0143 |
| 0.0064 | 13.0 | 32565 | 0.0161 |
| 0.0027 | 14.0 | 35070 | 0.0165 |
| 0.0083 | 15.0 | 37575 | 0.0189 |
| 0.0 | 16.0 | 40080 | 0.0172 |
| 0.0003 | 17.0 | 42585 | 0.0175 |
| 0.0021 | 18.0 | 45090 | 0.0213 |
| 0.0001 | 19.0 | 47595 | 0.0190 |
| 0.0 | 20.0 | 50100 | 0.0181 |
| 0.0 | 21.0 | 52605 | 0.0179 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
numind/NuNER-BERT-v1.0 | numind | 2024-03-19T19:20:53Z | 686 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"token-classification",
"entity-recognition",
"foundation-model",
"BERT",
"generic",
"en",
"dataset:numind/NuNER",
"arxiv:2402.15343",
"license:mit",
"region:us"
] | token-classification | 2024-03-04T14:48:47Z | ---
language:
- en
license: mit
tags:
- token-classification
- entity-recognition
- foundation-model
- feature-extraction
- BERT
- generic
datasets:
- numind/NuNER
pipeline_tag: token-classification
inference: false
---
# SOTA Entity Recognition English Foundation Model by NuMind 🔥
This is the **BERT** model from our [**Paper**](https://arxiv.org/abs/2402.15343): **NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**
<u>**This is the model used in Section 4.2 when comparing against TadNER.**</u>
For other sections, [NuNER v1.0](https://huggingface.co/numind/NuNER-v1.0) is used.
**Checkout other models by NuMind:**
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## About
[bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) fine-tuned on [NuNER data](https://huggingface.co/datasets/numind/NuNER).
**Metrics:**
Read more about evaluation protocol datasets in Section 4.2 of our [paper](https://arxiv.org/abs/2402.15343).
## Usage
Embeddings can be used out of the box or fine-tuned on specific datasets.
Get embeddings:
```python
import torch
import transformers
model = transformers.AutoModel.from_pretrained(
'numind/NuNER-BERT-v1.0',
output_hidden_states=True
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
'numind/NuNER-BERT-v1.0'
)
text = [
"NuMind is an AI company based in Paris and USA.",
"See other models from us on https://huggingface.co/numind"
]
encoded_input = tokenizer(
text,
return_tensors='pt',
padding=True,
truncation=True
)
output = model(**encoded_input)
# for better quality
emb = torch.cat(
(output.hidden_states[-1], output.hidden_states[-7]),
dim=2
)
# for better speed
# emb = output.hidden_states[-1]
```
## Citation
```
@misc{bogdanov2024nuner,
title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data},
author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard},
year={2024},
eprint={2402.15343},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
syf97/adapter_course | syf97 | 2024-03-19T19:13:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:yam-peleg/Experiment26-7B",
"base_model:adapter:yam-peleg/Experiment26-7B",
"region:us"
] | null | 2024-03-19T19:12:14Z | ---
library_name: peft
base_model: yam-peleg/Experiment26-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **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]
### Framework versions
- PEFT 0.8.2 |
Comert77/Zz | Comert77 | 2024-03-19T19:12:52Z | 0 | 0 | asteroid | [
"asteroid",
"summarization",
"ae",
"dataset:zeroshot/twitter-financial-news-sentiment",
"license:mit",
"region:us"
] | summarization | 2024-03-19T19:12:22Z | ---
license: mit
datasets:
- zeroshot/twitter-financial-news-sentiment
language:
- ae
metrics:
- bleu
library_name: asteroid
pipeline_tag: summarization
--- |
geekym15/gemma2b_fine_tuned_llm_tutor-V2 | geekym15 | 2024-03-19T19:11:12Z | 122 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T19:07:41Z | ---
library_name: transformers
tags: []
---
# 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V | cerebras | 2024-03-19T19:07:20Z | 164 | 0 | transformers | [
"transformers",
"pytorch",
"clip_vision_model",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T17:01:00Z | ---
{}
---
# Model Card for Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V
The checkpoints here are for the vision encoder part of **cerebras/Cerebras-LLaVA-7B**.
**Note**: _ShareGPT4V_ is added to the model name to ensure correct loading of checkpoints in [LLaVA source repo](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/builder.py#L8)
For full details of this model and training details, please read our upcoming blog post.
## License:
Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Model Architecture
Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V is a transformer model based on CLIP-VisionModel-Large(openai/clip-vit-large-patch14-336). It handles images of size 336 x 336 with patch size of 14
## Intended Use
_Primary intended uses_: The primary use of LLaVA is research on large multimodal models and chatbots.
_Primary intended users_: The primary intended users of the model are researchers(both academic and industry) in computer vision, natural language processing, machine learning, and artificial intelligence
## Limitations and Bias
The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models for their applications or any use case that may cause deliberate or unintentional harm to others. This model is for demonstration purpose only.
|
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_8_0.0002 | ferrazzipietro | 2024-03-19T19:07:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T19:05:53Z | ---
library_name: transformers
tags: []
---
# 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]
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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
<!-- 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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## Citation [optional]
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|
matanby/unsafe-diffusion | matanby | 2024-03-19T19:05:00Z | 110 | 0 | diffusers | [
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-02-27T17:02:10Z | This dummy model demonstrateד the potential dangers of unsafe pickle loading.
In this case a simple warning message is displayed, nothing more. |
xuweilai1991/wav2vec2-large-mms-1b-turkish-colab | xuweilai1991 | 2024-03-19T19:03:16Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_6_1",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-03-09T21:43:27Z | ---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_6_1
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-turkish-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_6_1
type: common_voice_6_1
config: tr
split: test
args: tr
metrics:
- name: Wer
type: wer
value: 0.2164232458380145
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-mms-1b-turkish-colab
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1535
- Wer: 0.2164
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7812 | 0.92 | 100 | 0.1811 | 0.2468 |
| 0.2821 | 1.83 | 200 | 0.1644 | 0.2291 |
| 0.2647 | 2.75 | 300 | 0.1574 | 0.2214 |
| 0.2407 | 3.67 | 400 | 0.1535 | 0.2164 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
cerebras/Cerebras-LLaVA-7B | cerebras | 2024-03-19T19:01:19Z | 11 | 2 | transformers | [
"transformers",
"pytorch",
"llava",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T16:59:07Z | ---
{}
---
# Model Card for cerebras/Cerebras-LLaVA-7B
The checkpoints consists of Language encoder and projector weights of multimodal LLaVA-7B model trained with our Cerebras implementation and training recipe.
The vision encoder checkpoints for this model can be found at [cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V](https://huggingface.co/cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V)
**Note**: _ShareGPT4V_ is added to the vision model name to ensure correct loading of checkpoints in [LLaVA source repo](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/builder.py#L8)
For full details of this model and training details, please read our upcoming blog post.
## License
Cerebras-Llava is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
## Model Architecture
Cerebras-LLaVA-7B is a transformer model with the following architecture details
* Vision encoder: [CLIP-VisionModel-Large](cerebras/Cerebras-ViT-L-336-patch14-llava7b-ShareGPT4V). It handles images of size 336 x 336 with patch size of 14
* Large Language Model: Pretrained from Vicuna-7B checkpoints and instruction finetuned on various datasets.
* Projector: the projector module that connects the LLM and Vision encoder part consists of two linear layers with gelu activation (mlp2x-gelu)
## Loading the model
This model can directly be loaded using the [LLaVa source code repository](https://github.com/haotian-liu/LLaVA). For installation, please refer to the [instructions in source code repository](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#install).
We perform all our evaluations using the LLaVA source code repository scripts.
```
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
model_path = "cerebras/Cerebras-LLaVA-7B"
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_path=model_path,
model_base=None,
model_name=get_model_name_from_path(model_path)
)
```
## Intended Use
_Primary intended uses_: The primary use of LLaVA is research on large multimodal models and chatbots.
_Primary intended users_: The primary intended users of the model are researchers(both academic and industry) in computer vision, natural language processing, machine learning, and artificial intelligence
## Limitations and Bias
The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text.
We recommend that users exercise caution when using these models for their applications or any use case that may cause deliberate or unintentional harm to others. This model is for demonstration purpose only.
## Acknowledgements
We are thankful to all Cerebras engineers that made this work possible. |
microsoft/Mistral-7B-v0.1-onnx | microsoft | 2024-03-19T18:56:14Z | 1 | 12 | null | [
"onnx",
"mistral",
"onnxruntime",
"llm",
"en",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-11-14T17:07:36Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
language:
- en
tags:
- mistral
- onnxruntime
- onnx
- llm
---
#### This is an optimized version of the Mistral 7B model, available on this repository: https://huggingface.co/mistralai/Mistral-7B-v0.1 and under the license on such repository. Microsoft permits you to use, modify, redistribute, and create derivatives of Microsoft's contributions to the optimized version subject to the restrictions and disclaimers of warranty and liability in license agreement.
# Mistral-7b for ONNX Runtime
## Introduction
This repository hosts the optimized versions of **Mistral-7B-v0.1** to accelerate inference with ONNX Runtime CUDA execution provider.
See the [usage instructions](#usage-example) for how to inference this model with the ONNX files hosted in this repository.
## Model Description
- **Developed by:** MistralAI
- **Model type:** Pretrained generative text model
- **License:** Apache 2.0 License
- **Model Description:** This is a conversion of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) for [ONNX Runtime](https://github.com/microsoft/onnxruntime) inference with CUDA execution provider.
## Performance Comparison
#### Latency for token generation
Below is average latency of generating a token using a prompt of varying size using NVIDIA A100-SXM4-80GB GPU, taken from the [ORT benchmarking script for Mistral](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/llama/README.md#benchmark-mistral)
| Prompt Length | Batch Size | PyTorch 2.1 torch.compile | ONNX Runtime CUDA |
|-------------|------------|----------------|-------------------|
| 32 | 1 | 32.58ms | 12.08ms |
| 256 | 1 | 54.54ms | 23.20ms |
| 1024 | 1 | 100.6ms | 77.49ms |
| 2048 | 1 | 236.8ms | 144.99ms |
| 32 | 4 | 63.71ms | 15.32ms |
| 256 | 4 | 86.74ms | 75.94ms |
| 1024 | 4 | 380.2ms | 273.9ms |
| 2048 | 4 | N/A | 554.5ms |
## Usage Example
Following the [benchmarking instructions](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/llama/README.md#mistral). Example steps:
1. Clone onnxruntime repository.
```shell
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime
```
2. Install required dependencies
```shell
python3 -m pip install -r onnxruntime/python/tools/transformers/models/llama/requirements-cuda.txt
```
5. Inference using manual model API, or use Hugging Face's ORTModelForCausalLM
```python
from optimum.onnxruntime import ORTModelForCausalLM
from onnxruntime import InferenceSession
from transformers import AutoConfig, AutoTokenizer
sess = InferenceSession("Mistral-7B-v0.1.onnx", providers = ["CUDAExecutionProvider"])
config = AutoConfig.from_pretrained("mistralai/Mistral-7B-v0.1")
model = ORTModelForCausalLM(sess, config, use_cache = True, use_io_binding = True)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
inputs = tokenizer("Instruct: What is a fermi paradox?\nOutput:", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
e22vvb/ALL_bart_15_spider_new2 | e22vvb | 2024-03-19T18:51:58Z | 119 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-19T17:12:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ALL_bart_15_spider_new2
results: []
---
<!-- 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. -->
# ALL_bart_15_spider_new2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4402
- Rouge2 Precision: 0.4522
- Rouge2 Recall: 0.3179
- Rouge2 Fmeasure: 0.3513
## 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: 30
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.7693 | 1.0 | 647 | 0.3306 | 0.3852 | 0.2571 | 0.2875 |
| 0.166 | 2.0 | 1294 | 0.3361 | 0.3785 | 0.2577 | 0.2858 |
| 0.1247 | 3.0 | 1941 | 0.3572 | 0.4176 | 0.2801 | 0.3132 |
| 0.0844 | 4.0 | 2588 | 0.3678 | 0.4189 | 0.2882 | 0.32 |
| 0.0711 | 5.0 | 3235 | 0.3778 | 0.4313 | 0.3021 | 0.3338 |
| 0.0619 | 6.0 | 3882 | 0.3928 | 0.4335 | 0.3012 | 0.3337 |
| 0.0474 | 7.0 | 4529 | 0.3987 | 0.4415 | 0.3064 | 0.3395 |
| 0.041 | 8.0 | 5176 | 0.4098 | 0.4289 | 0.2963 | 0.3288 |
| 0.0371 | 9.0 | 5823 | 0.4180 | 0.4404 | 0.3134 | 0.3447 |
| 0.0331 | 10.0 | 6470 | 0.4197 | 0.4359 | 0.3055 | 0.3375 |
| 0.0268 | 11.0 | 7117 | 0.4272 | 0.4406 | 0.3084 | 0.3409 |
| 0.025 | 12.0 | 7764 | 0.4337 | 0.4424 | 0.3105 | 0.3427 |
| 0.0229 | 13.0 | 8411 | 0.4355 | 0.4383 | 0.3104 | 0.3416 |
| 0.0198 | 14.0 | 9058 | 0.4391 | 0.4471 | 0.3157 | 0.3481 |
| 0.0187 | 15.0 | 9705 | 0.4402 | 0.4522 | 0.3179 | 0.3513 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.7.dev0
- Tokenizers 0.13.3
|
sarthakharne/bert-base-100-ep-pretrain-on-textbooks | sarthakharne | 2024-03-19T18:46:22Z | 177 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-03-19T18:44:43Z | ---
library_name: transformers
tags: []
---
# 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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### 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
<!-- 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] |
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_2_0.0002 | ferrazzipietro | 2024-03-19T18:43:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T18:42:43Z | ---
library_name: transformers
tags: []
---
# 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]
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## Model Card Contact
[More Information Needed]
|
Bakanayatsu/Fimbulvetr-Kuro-Lotus-10.7B-GGUF-imatrix | Bakanayatsu | 2024-03-19T18:42:32Z | 125 | 5 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-03-16T10:40:23Z | Original: [Fimbulvetr-Kuro-Lotus-10.7B](https://huggingface.co/saishf/Fimbulvetr-Kuro-Lotus-10.7B)
GGUF: [Fimbulvetr-Kuro-Lotus-10.7B-GGUF](https://huggingface.co/saishf/Fimbulvetr-Kuro-Lotus-10.7B-GGUF)
Imatrix: Here
3/19/2024: Fixed the incoherent generation when context is greater than 4096 with koboldcpp --contextsize |
adasgaleus/20240319175245_strong_kingma | adasgaleus | 2024-03-19T18:33:05Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-03-19T18:32:45Z | ---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240319175245_strong_kingma
results: []
---
<!-- 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. -->
# 20240319175245_strong_kingma
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0364
- Precision: 0.9707
- Recall: 0.9776
- F1: 0.9741
- Accuracy: 0.9871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 350
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0733 | 0.09 | 300 | 0.0625 | 0.9531 | 0.9570 | 0.9550 | 0.9775 |
| 0.0758 | 0.17 | 600 | 0.0667 | 0.9522 | 0.9518 | 0.9520 | 0.9760 |
| 0.0668 | 0.26 | 900 | 0.0587 | 0.9549 | 0.9615 | 0.9582 | 0.9789 |
| 0.0616 | 0.35 | 1200 | 0.0586 | 0.9551 | 0.9655 | 0.9603 | 0.9798 |
| 0.0583 | 0.44 | 1500 | 0.0521 | 0.9588 | 0.9672 | 0.9630 | 0.9813 |
| 0.0548 | 0.52 | 1800 | 0.0494 | 0.9611 | 0.9681 | 0.9646 | 0.9823 |
| 0.0506 | 0.61 | 2100 | 0.0462 | 0.9638 | 0.9704 | 0.9671 | 0.9834 |
| 0.0472 | 0.7 | 2400 | 0.0432 | 0.9651 | 0.9736 | 0.9693 | 0.9846 |
| 0.0436 | 0.78 | 2700 | 0.0402 | 0.9674 | 0.9748 | 0.9711 | 0.9855 |
| 0.0415 | 0.87 | 3000 | 0.0380 | 0.9704 | 0.9753 | 0.9728 | 0.9865 |
| 0.039 | 0.96 | 3300 | 0.0364 | 0.9707 | 0.9776 | 0.9741 | 0.9871 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0a0+6a974be
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_32_64_0.01_8_0.0002 | ferrazzipietro | 2024-03-19T18:31:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T18:31:23Z | ---
library_name: transformers
tags: []
---
# 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 meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### 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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
theirislin/Derberta-SynCPKL | theirislin | 2024-03-19T18:31:56Z | 113 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T18:26:48Z | ---
library_name: transformers
tags: []
---
# 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
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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]
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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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|
tatsunori/distilbert-base-uncased-distilled-clinc | tatsunori | 2024-03-19T18:31:09Z | 118 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T18:26:47Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- 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-distilled-clinc
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: 0.2223
- Accuracy: 0.9445
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.2786 | 0.7235 |
| 1.5922 | 2.0 | 636 | 0.6337 | 0.8652 |
| 1.5922 | 3.0 | 954 | 0.3711 | 0.9171 |
| 0.5859 | 4.0 | 1272 | 0.2814 | 0.9316 |
| 0.288 | 5.0 | 1590 | 0.2498 | 0.9381 |
| 0.288 | 6.0 | 1908 | 0.2378 | 0.9406 |
| 0.2186 | 7.0 | 2226 | 0.2298 | 0.9413 |
| 0.1965 | 8.0 | 2544 | 0.2260 | 0.9435 |
| 0.1965 | 9.0 | 2862 | 0.2234 | 0.9445 |
| 0.1884 | 10.0 | 3180 | 0.2223 | 0.9445 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
mpasila/NordicAlpaca-Finnish-V1-7B-LoRA | mpasila | 2024-03-19T18:30:36Z | 2 | 0 | peft | [
"peft",
"safetensors",
"fi",
"dataset:pinzhenchen/alpaca-cleaned-fi",
"base_model:HPLT/gpt-7b-nordic-prerelease",
"base_model:adapter:HPLT/gpt-7b-nordic-prerelease",
"license:apache-2.0",
"region:us"
] | null | 2024-03-19T14:23:51Z | ---
language:
- fi
library_name: peft
base_model: HPLT/gpt-7b-nordic-prerelease
license: apache-2.0
datasets:
- pinzhenchen/alpaca-cleaned-fi
---
# Model Card for NordicAlpaca-Finnish-V1-7B-LoRA
LoRA trained in 4-bit using [HPLT/gpt-7b-nordic-prerelease](https://huggingface.co/HPLT/gpt-7b-nordic-prerelease/) as the base model for 1 epoch. Dataset used with the LoRA is [pinzhenchen/alpaca-cleaned-fi](https://huggingface.co/datasets/pinzhenchen/alpaca-cleaned-fi/).
It uses Alpaca format but with a translated instruction at the start:
```
{
"instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
"instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}
```
Using the following settings:
```json
{
"lora_name": "Alpaca-Finnish-v1",
"always_override": false,
"q_proj_en": true,
"v_proj_en": true,
"k_proj_en": false,
"o_proj_en": false,
"gate_proj_en": false,
"down_proj_en": false,
"up_proj_en": false,
"save_steps": 250.0,
"micro_batch_size": 4,
"batch_size": 128,
"epochs": 3.0,
"learning_rate": "3e-4",
"lr_scheduler_type": "linear",
"lora_rank": 256,
"lora_alpha": 512,
"lora_dropout": 0.05,
"cutoff_len": 384,
"dataset": "alpaca_data_cleaned.fi",
"eval_dataset": "None",
"format": "alpaca-format-finnish",
"eval_steps": 100.0,
"raw_text_file": "None",
"overlap_len": 128,
"newline_favor_len": 128,
"higher_rank_limit": false,
"warmup_steps": 100.0,
"optimizer": "adamw_torch",
"hard_cut_string": "\\n\\n\\n",
"train_only_after": "",
"stop_at_loss": 0,
"add_eos_token": false,
"min_chars": 0.0,
"report_to": "None"
}
```
### Framework versions
- PEFT 0.8.2 |
AmilaUvaz/autotrain-w7ckn-wbqd2 | AmilaUvaz | 2024-03-19T18:28:49Z | 1 | 0 | diffusers | [
"diffusers",
"autotrain",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:openrail++",
"region:us"
] | text-to-image | 2024-03-19T18:28:46Z |
---
tags:
- autotrain
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: <1girl, 24 years old Latina fitness girl called Rishi. oblong face shape, Almond-shaped, brown, medium-sized, with a slight upward tilt, Thin and arched eyebrows, curved nose, natural lips>
license: openrail++
---
# AutoTrain LoRA DreamBooth - AmilaUvaz/autotrain-w7ckn-wbqd2
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on <1girl, 24 years old Latina fitness girl called Rishi. oblong face shape, Almond-shaped, brown, medium-sized, with a slight upward tilt, Thin and arched eyebrows, curved nose, natural lips> using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
|
tatsunori/distilbert-base-uncased-finetuned-clinc | tatsunori | 2024-03-19T18:26:16Z | 12 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T16:39:30Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- 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-clinc
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: 0.7965
- Accuracy: 0.9171
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.3098 | 0.7152 |
| 3.8102 | 2.0 | 636 | 1.9031 | 0.8465 |
| 3.8102 | 3.0 | 954 | 1.1806 | 0.8887 |
| 1.7245 | 4.0 | 1272 | 0.8839 | 0.9110 |
| 0.9286 | 5.0 | 1590 | 0.7965 | 0.9171 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Crystalcareai/Gemma-7b-Fixed | Crystalcareai | 2024-03-19T18:25:51Z | 8 | 3 | transformers | [
"transformers",
"safetensors",
"gemmoe",
"text-generation",
"custom_code",
"arxiv:2305.14314",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-03-17T08:25:22Z | ---
library_name: transformers
---
GEMMA IS NOW FIXED WITHIN TRANSFORMERS - DISREGARD THIS REPO
# Reupload of Google Gemma - Find original readme below.
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning examples
You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314)
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(**input_text, return_tensors="pt")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **54.0** | **56.4** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
|
abh1nav/tamil_llama-v1 | abh1nav | 2024-03-19T18:19:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:abhinand/tamil-llama-7b-instruct-v0.1",
"base_model:adapter:abhinand/tamil-llama-7b-instruct-v0.1",
"region:us"
] | null | 2024-03-15T16:34:32Z | ---
library_name: peft
base_model: abhinand/tamil-llama-7b-instruct-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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]
#### 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]
<!-- 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
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### Framework versions
- PEFT 0.9.0 |
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_32_64_0.01_2_0.0002 | ferrazzipietro | 2024-03-19T18:09:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T18:08:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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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
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|
dyang415/empower-functions-more-tools-parallel-1775 | dyang415 | 2024-03-19T18:04:52Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mixtral",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-03-19T18:04:33Z | ---
library_name: peft
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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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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[More Information Needed]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0 |
sarthakharne/bert-base-95-ep-pretrain-on-textbooks | sarthakharne | 2024-03-19T18:02:33Z | 194 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-03-19T18:00:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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jjezabek/ai_swe-openchat-3.5-0106-3-epochs | jjezabek | 2024-03-19T18:00:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T18:00:42Z | ---
library_name: transformers
tags: []
---
# 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]
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<!-- Relevant interpretability work for the model goes here -->
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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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|
dranger003/Qwen1.5-72B-Chat-iMat.GGUF | dranger003 | 2024-03-19T17:58:30Z | 112 | 11 | gguf | [
"gguf",
"text-generation",
"base_model:Qwen/Qwen1.5-72B-Chat",
"base_model:quantized:Qwen/Qwen1.5-72B-Chat",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-02-08T22:53:03Z | ---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
pipeline_tag: text-generation
library_name: gguf
base_model: Qwen/Qwen1.5-72B-Chat
---
<u>**NOTE**</u>: You will need a recent build of llama.cpp to run these quants (i.e. at least commit `494c870`).
**2024-03-19**: Uploading new quants retrained on `wiki.train.raw` for ~100K tokens.
**2024-03-07**: Refreshing quants using latest build as things seem to have stabilized a bit now.
GGUF importance matrix (imatrix) quants for https://huggingface.co/Qwen/Qwen1.5-72B-Chat
* The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a [general purpose imatrix calibration dataset](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384).
* The [imatrix is being used on the K-quants](https://github.com/ggerganov/llama.cpp/pull/4930) as well.
| Layers | Context | [Template](https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/tokenizer_config.json#L31) |
| --- | --- | --- |
| <pre>80</pre> | <pre>32768</pre> | <pre><\|im_start\|>system<br>{instructions}<\|im_end\|><br><\|im_start\|>user<br>{prompt}<\|im_end\|><br><\|im_start\|>assistant<br>{response}</pre> |
|
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_32_32_0.01_8_0.0002 | ferrazzipietro | 2024-03-19T17:58:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T17:57:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
arh/distilbert-emotion | arh | 2024-03-19T17:52:41Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T17:48:19Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.937
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1395
- Accuracy: 0.937
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 250 | 0.1824 | 0.9305 |
| 0.3392 | 2.0 | 500 | 0.1395 | 0.937 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
peldrak/mask2former-large-ade-finetuned-coastTrain-grCoastline | peldrak | 2024-03-19T17:48:46Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"mask2former",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T00:23:04Z | ---
library_name: transformers
tags: []
---
# 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. -->
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|
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_32_32_0.01_4_0.0002 | ferrazzipietro | 2024-03-19T17:47:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T17:46:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
samhitmantrala/pr_5 | samhitmantrala | 2024-03-19T17:43:47Z | 181 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T17:43:14Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: pr_5
results: []
---
<!-- 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. -->
# pr_5
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9638
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 7 | 1.1487 |
| No log | 2.0 | 14 | 1.0146 |
| No log | 3.0 | 21 | 0.9638 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Azarthehulk/NST | Azarthehulk | 2024-03-19T17:42:44Z | 0 | 0 | null | [
"en",
"region:us"
] | null | 2024-03-19T17:25:23Z | ---
language:
- en
---
 |
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_32_64_0.01_4_0.0002 | ferrazzipietro | 2024-03-19T17:39:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:33:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## 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]
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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. -->
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#### Metrics
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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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[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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|
martineden/BERTJson-iett | martineden | 2024-03-19T17:38:49Z | 116 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-03-19T17:38:29Z | ---
library_name: transformers
tags: []
---
# 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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- **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]
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[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]
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[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
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
12345deena/t5baseflan | 12345deena | 2024-03-19T17:32:42Z | 162 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-19T13:55:36Z | ---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
model-index:
- name: t5baseflan
results: []
---
<!-- 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. -->
# t5baseflan
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.9888
- eval_rouge1: 42.8793
- eval_rouge2: 21.0178
- eval_rougeL: 27.8796
- eval_rougeLsum: 38.8123
- eval_gen_len: 198.6808
- eval_runtime: 1542.7666
- eval_samples_per_second: 0.658
- eval_steps_per_second: 0.165
- epoch: 1.0
- step: 515
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_32_64_0.01_2_0.0002 | ferrazzipietro | 2024-03-19T17:32:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T17:26:11Z | ---
library_name: transformers
tags: []
---
# 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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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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- **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
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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
### 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]
#### 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:**
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**APA:**
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## Glossary [optional]
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|
prajwalJumde/rap_phase2_19march_5i | prajwalJumde | 2024-03-19T17:31:30Z | 123 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-03-19T15:55:07Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: rap_phase2_19march_5i
results: []
---
<!-- 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. -->
# rap_phase2_19march_5i
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0178
## 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: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1477 | 1.0 | 4295 | 0.0859 |
| 0.0574 | 2.0 | 8590 | 0.0485 |
| 0.0295 | 3.0 | 12885 | 0.0388 |
| 0.0311 | 4.0 | 17180 | 0.0336 |
| 0.0169 | 5.0 | 21475 | 0.0275 |
| 0.0169 | 6.0 | 25770 | 0.0245 |
| 0.0037 | 7.0 | 30065 | 0.0204 |
| 0.0024 | 8.0 | 34360 | 0.0164 |
| 0.004 | 9.0 | 38655 | 0.0187 |
| 0.0 | 10.0 | 42950 | 0.0178 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
sdidier-dev/q-Taxi-v1 | sdidier-dev | 2024-03-19T17:31:27Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-19T17:31:25Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sdidier-dev/q-Taxi-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
xuweilai1991/w2v-bert-2.0-mongolian-colab-CV16.0 | xuweilai1991 | 2024-03-19T17:29:35Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-12T20:50:46Z | ---
library_name: transformers
tags: []
---
# 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] |
jonbarlow/distilbert_coarse5_js_1.1 | jonbarlow | 2024-03-19T17:25:08Z | 104 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T17:12:51Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_coarse5_js_1.1
results: []
---
<!-- 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_coarse5_js_1.1
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: 0.6826
- Accuracy: 0.8039
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 364 | 0.9021 | 0.6830 |
| 1.0729 | 2.0 | 728 | 0.6936 | 0.7712 |
| 0.6279 | 3.0 | 1092 | 0.6766 | 0.7745 |
| 0.6279 | 4.0 | 1456 | 0.6633 | 0.7941 |
| 0.4531 | 5.0 | 1820 | 0.6691 | 0.8137 |
| 0.3527 | 6.0 | 2184 | 0.6826 | 0.8039 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
dmdoy/Emotion_Recognition_From_Speech | dmdoy | 2024-03-19T17:21:43Z | 0 | 2 | null | [
"music",
"code",
"audio-classification",
"en",
"arxiv:1912.10458",
"arxiv:1909.05645",
"arxiv:1701.08071",
"license:gpl-3.0",
"region:us"
] | audio-classification | 2024-03-19T15:41:25Z | ---
license: gpl-3.0
language:
- en
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- music
- code
---
# Emotion Recognition From Speech (V1.0)
<p align="justify">The understanding of emotions from voice by a human brain are normal instincts of human beings, but automating the process of emotion recognition from speech without referring any language or linguistic information remains an uphill grind. In the research work presented based on the input speech, I am trying to predict one of the six types of emotions (sad, neutral, happy, fear, angry, disgust). The diagram given below explain how emotion recognition from speech works. The audio features are extracted from input speech, then those features are passed to the emotion recognition model which predicts one of the six emotions for the given input speech.</p>
# Motivation
<p align="justify">Most of the smart devices or voice assistants or robots present in the world are not smart enough to understand the emotions. They are just like command and follow devices they have no emotional intelligence. When people are talking to each other based on the voice they understand situation and react to it, for instance if someone is angry then other person will try to clam him by conveying in soft tone, these kind of harmonic changes are not possible with smart devices or voice assistants as they lack emtional intelligence. So adding emotions and making devices understand emotions will take them one step further to human like intelligence.</p>
# Application
<p align="justify">There are tonnes of applicates based on one can imagine. Few applications based on my thinking are human computer interaction using voice, home automation, anger/stress management by decoding emotions from voice, emotion recognition can help in detecting fear and cops can used this system to check if dialer is feared by some one or its just a normal call to register a complain, Marketing companies can use emotions to sell products based on user mood, autonomous vehicles can detect user emotion and adjust the speed of vehicles, It can help in solving psychological or depression problems. These are few applications according to me but there can be many more as voice based systems are increasing, even voice bsed chatting is common on social media platforms like clubhouse, discord, twitch, and others.</p>
# Libraries and coding language used for the project
<a href="http://ffmpeg.org/"><img src="https://img.shields.io/badge/ffmpeg-green?style=flat&logo=ffmpeg&labelColor=green"></a>
<a href="https://pandas.pydata.org/"><img src="https://img.shields.io/badge/pandas-darkblue?style=flat&logo=pandas&labelColor=darkblue"></a>
<a href="https://numpy.org/"><img src="https://img.shields.io/badge/numpy-skyblue?style=flat&logo=numpy&labelColor=skyblue"></a>
<a href="https://www.tensorflow.org/"><img src="https://img.shields.io/badge/tensorflow-orange?style=flat&logo=tensorflow&labelColor=orange"></a>
<a href="https://docs.python.org/3/library/os.html"><img src="https://img.shields.io/badge/os-lightyellow?style=flat&logo=os&labelColor=lightyellow"></a>
<a href="https://docs.python.org/3/library/time.html"><img src="https://img.shields.io/badge/time-lightgreen?style=flat&logo=time&labelColor=lightgreen"></a>
<a href="https://librosa.org/"><img src="https://img.shields.io/badge/librosa-pink?style=flat&logo=librosa&labelColor=pink"></a>
<a href="https://docs.python.org/3/library/warnings.html"><img src="https://img.shields.io/badge/warnings-lightred?style=flat&logo=warings&labelColor=lightred"></a>
<a href="https://docs.python.org/3/library/base64.html"><img src="https://img.shields.io/badge/base64-lightgrey?style=flat&logo=base64&labelColor=lightgrey"></a>
<a href="https://pypi.org/project/google-colab/"><img src="https://img.shields.io/badge/google-colab-lightorange?style=flat&logo=google-colab&labelColor=lightorange"></a>
<a href="https://docs.python.org/3/library/glob.html"><img src="https://img.shields.io/badge/glob-lightgrey?style=flat&logo=glob&labelColor=lightgrey"></a>
<a href="https://docs.python.org/3/library/re.html"><img src="https://img.shields.io/badge/regex-darkgreen?style=flat&logo=regex&labelColor=darkgreen"></a>
<a href="https://scikit-learn.org/stable/"><img src="https://img.shields.io/badge/scikit-learn-darkorange?style=flat&logo=scikit-learn&labelColor=darkorange"></a>
<a href="https://keras.io/"><img src="https://img.shields.io/badge/keras-darkred?style=flat&logo=keras&labelColor=darkred"></a>
<a href="https://www.scipy.org/"><img src="https://img.shields.io/badge/scipy-violet?style=flat&logo=scipy&labelColor=violet"></a>
<a href="https://docs.python.org/3/library/io.html"><img src="https://img.shields.io/badge/io-grey?style=flat&logo=io&labelColor=grey"></a>
<a href="https://ipython.org/"><img src="https://img.shields.io/badge/ipython-purple?style=flat&logo=ipython&labelColor=purple"></a>
<a href="https://matplotlib.org/"><img src="https://img.shields.io/badge/matplotlib-brown?style=flat&logo=matplotlib&labelColor=brown"></a>
<a href="https://www.python.org/doc/"><img src="https://img.shields.io/badge/python3-yellow?style=flat&logo=python3&labelColor=yellow"></a>
# Dataset description
<p align="justify">I have used four datasets and all four datasets are freely available to downloaded from kaggle website. So I have downloaded the data, extracted and stored in my google drive.</p>
1) Ryerson Audio Visual Database of Emotional Speech and Song (Ravdess) dataset description:<br>
Dataset link to download: "https://www.kaggle.com/uwrfkaggler/ravdess-emotional-speech-audio" <br>
Dataset stored on google drive at path: "/content/drive/MyDrive/Audiofiles/audio_speech_actors_01-24/"<br>
Dataset contains sub folders and file names as example in numbers format 03-01-01-01-01-01-01.wav.<br>
Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).<br>
So based on the number there is a identifier for each number and its meaning are as follows:
* Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
* Vocal channel (01 = speech, 02 = song).
* Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
* Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
* Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
* Repetition (01 = 1st repetition, 02 = 2nd repetition).
* Therefore file 03-01-01-01-01-01-01.wav can be deduced as 03=audio-only, 01=speech, 01=neutral, 01=normal, 01=statement kids and 01=1st repetition.
2) Crowd sourced Emotional Mutimodal Actors Dataset (CREMA-D) dataset description:<br>
Dataset link to download: "https://www.kaggle.com/ejlok1/cremad" <br>
Dataset stored on google drive at path: "/content/drive/MyDrive/Audiofiles/AudioWAV/"<br>
The format of files is 1001_DFA_ANG_XX.wav, where ANG stands for angry emotion.<br>
Similarly different emotion mappings are as follows:<br>
{'SAD':'sad','ANG':'angry','DIS':'disgust','FEA':'fear','HAP':'happy','NEU':'neutral'}
3) Toronto emotional speech set (Tess) dataset description:<br>
Dataset link to download: "https://www.kaggle.com/ejlok1/toronto-emotional-speech-set-tess" <br>
Dataset stored on google drive at path: "/content/drive/MyDrive/Audiofiles/TESS Toronto emotional speech set data/"<br>
There are folders in format OAF_angry, OAF_neural, OAF_disgust, YAF_sad and so on, where name after the underscore of the folder name contains the emotion information, so the name after the underscore of the folder name is taken and files residing insider the folders are labeled accordingly.
4) Surrey Audio Visual Expressed Emotion (Savee) dataset description:<br>
Dataset link to download: "https://www.kaggle.com/ejlok1/surrey-audiovisual-expressed-emotion-savee" <br>
Dataset stored on google drive at path: "/content/drive/MyDrive/Audiofiles/ALL/"<br>
The files are in a format DC_a01.wav where a single character contains the emotion information , for example character 'a' after underscore in the file name "DC_a01.wav" means emotion is angry.<br>
Similarly different emotion mappings are as follows:<br>
{'a':'anger','d':'disgust','f':'fear','h':'happiness','n':'neutral','sa':'sadness','su':'surprise'}
# Universal decorator fucntion to calculate total time
```
def calc_time(func):
def inner(*args, **kwargs):
st = time.time()
result = func(*args,**kwargs)
end = time.time()-st
print("Total time required: {:.3f} ms".format(end * 1000))
return result
return inner
```
# Description of important functions present in code (Model design and evaluation):
<p align="justify">There are many functions in the program as functional programming style is used. Here I am going to describe a few important functions which call other functions inside the functions and generate files and results. Detailed description of each function and its use can be found in the code file.</p>
* Audio_features_extract() this function is used to extract audio features and generates a csv file at path "/content/drive/MyDrive/Audiofiles
/Audio_features_All_pr.csv" which contains audio features and their respective label information.
* Below given image shows snapshot of the csv file, the file has a total of 33954 rows × 179 columns.
* The csv file is loaded using pandas library, additional_preprocess() function carries out Exploratory Data Analysis and drop emotions with limited samples to
avoid missclassifications and then dataset is divided into train, test and validation set.
* Below image gives the detailed description of the whole process.
* Deep learning model for speech recognition is trained using the training data and at every epoch or checkpoint validation accuracy is calucated. The epoch or
checkpoint which gives highest validation accuracy, the best model is saved for that epoch or checkpoint at path "/content/drive/ MyDrive/Audiofiles/
emotionrecognition.hdf5", the model giving highest validation accuracy is only saved.
# Description of testing model in real time:
Once the model is build and training is completed the emotion recognition model can be loaded from the path "/content/drive/MyDrive/Audiofiles/emotion-recognition .hdf5" and can be tested for the given input speech in real time.
* The data for real time model testing is recorded using the microphone.
* The code to record audio speech using microphone is integrated from the link "https://ricardodeazambuja.com/deep_learning/2019/03/09/audio_and_video_google_colab/".
* Then features are extracted from speech and passed to emotion recognition model which predicts one of the six emotions.
* Below figure shows the audio waveform and output of the emotion recognition model.
# Results
* Below figure shows the training, testing and validation accuracy achieved by the emotion recognition model.
* Below figure shows the classification report and it can be seen in the report that for all the classes the value is greater than 0.5 which means the model can predict the emotions accuratly to some extent. If the value is equal to 1.0 for all clases then it means model can predict accurrately always given the input speech. But its diffcult to achieve real time prediction and 100% accuracy on real time envoiurment as there is noise and many other factors which can affect the output. Given a challenge it can overcomed by training with big set of data in different languages to develop a universal model.
* The 0,1,2,3,4,5 in classification report resembles to different emotions which can be decoded from below image.
* Below figure shows output of confusion matrix.<br>
* Below figure shows the training loss and accuracy curves, despite the model giving the training accuracy of 100%, validation and testing accuracy is near to 75%-76%, my model gives the highest accuracy when compared to the authors who previously carried out the research work in this area.
* Additionally to check wheather the model can work for all types of voices and on unlabeled data a test was carried out using combination of different voices and unlabled data. Below figure shows the results.
* Below figures shows comparison of my model with other authors who worked previously in this area of emotion recognition from speech.
# Installation
To download and run my google colab file 1130532_ResearchMethodology_Project_Final.ipynb following changes need to be made:
* Frist and foremost make sure all neccessary libraries mentioned above are installed.
* To install any library in the computer machine just use command pip install library name.
* Then install the data from the following links:
* "https://www.kaggle.com/ejlok1/cremad"
* "https://www.kaggle.com/uwrfkaggler/ravdess-emotional-speech-audio"
* "https://www.kaggle.com/ejlok1/surrey-audiovisual-expressed-emotion-savee"
* "https://www.kaggle.com/ejlok1/toronto-emotional-speech-set-tess"
* Extract the downloaded data from the above given links
* Once the data is extracted just use my code and pass the proper path information to the functions.
* These paths are datapaths, csv file path and paths where reults are stored.
* Correct path information needs to be given in the functions ravdess_data(), crema_data(), tess_data(), saveee_data(), fetch_data(), Audio_features_extract(), audio_features_final(), emotion_recognition_model(), test_realtime(), evaluate_model(), unknown_audio() and diff_lang_test()
* Below given are snnipets of code where proper path information needs to be given for the above given functions.
* ```
ravdess = "/content/drive/MyDrive/Audiofiles/audio_speech_actors_01-24/"
```
* ```
crema = "/content/drive/MyDrive/Audiofiles/AudioWAV/"
```
* ```
tess = "/content/drive/MyDrive/Audiofiles/TESS Toronto emotional speech set data/"
```
* ```
savee = "/content/drive/MyDrive/Audiofiles/ALL/"
```
* ```
final_combined.to_csv("/content/drive/MyDrive/preprocesseddata.csv",index=False,header=True)
```
* ```
Features.to_csv('/content/drive/MyDrive/Audiofiles/Audio_features_All_pr.csv',index=False)
```
* ```
df = additional_preprocess("/content/drive/MyDrive/Audiofiles/Audio_features_All_pr.csv")
```
* ```
filepath = "/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5"
```
* ```
res_model = load_model("/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5")
```
* ```
os.chdir('/content/drive/MyDrive/Audiofiles/realtimetested')
```
* ```
np.save('/content/drive/MyDrive/Audiofiles/realtimetested/audiorec{}.npy'.format(len(files)),audio)
```
* ```
plt.savefig("audiorec{}.png".format(len(files)))
```
* ```
df["path"][i] = '/content/drive/MyDrive/Audiofiles/realtimetested/audiorec{}.npy'.format(len(files))
```
* ```
df.to_csv('/content/drive/MyDrive/Audiofiles/realtimetested/real_time_predicted_audio_features.csv', mode='a', index=False)
```
* ```
model = load_model("/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5")
```
* ```
path = '/content/drive/MyDrive/Audiofiles/realtimetested/testing on sample voices/'
```
* ```
Features.to_csv('/content/drive/MyDrive/Audiofiles/realtimetested/unkonwaudio.csv',index=False)
```
* ```
df = pd.read_csv('/content/drive/MyDrive/Audiofiles/realtimetested/unkonwaudio.csv')
```
* ```
res_model = load_model("/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5")
```
* So once the path information is given correctly its time to run the functions, run all the fuctions in the same sequence given in my colab file 1130532_ResearchMethodology_Project_Final.ipynb.
* If one dosent want to train the model just test the model then they can use the model file "emotion-recognition.hdf5", change the paths in test_realtime()
function and they can test the model.
* Following path needs to be changed:
* ```
res_model = load_model("/content/drive/MyDrive/Audiofiles/emotion-recognition.hdf5")
```
* ```
os.chdir('/content/drive/MyDrive/Audiofiles/realtimetested')
```
* ```
np.save('/content/drive/MyDrive/Audiofiles/realtimetested/audiorec{}.npy'.format(len(files)),audio)
```
* ```
plt.savefig("audiorec{}.png".format(len(files)))
```
* ```
df["path"][i] = '/content/drive/MyDrive/Audiofiles/realtimetested/audiorec{}.npy'.format(len(files))
```
* ```
df.to_csv('/content/drive/MyDrive/Audiofiles/realtimetested/real_time_predicted_audio_features.csv', mode='a', index=False)
```
* If you want to develop or implement or setupt the whole code then as mentioned give proper paths and run all the functions its done.
* Check out my colab file 1130532_ResearchMethodology_Project_Final.ipynb to see the time required by the individual process to complete.
* The main() function does all the work of training the model and evaluating the model. Once the main function completes running the model is file is generated and can used for real time testing.
* This is all about installation, building the model and feature extraction are one time process, once completed model is deployed in real time enviourment for testing and using the model for recognizing emotions from speech.
# usage
* As mentioned in the installation process, once libraries, datasets are downloaded, proper path information is given functions should be run in a sequence as mentioned in the colab file 1130532_ResearchMethodology_Project_Final.ipynb.
* Following are the functions that required to run and the sequence is same as mentioned below and in the colab file 1130532_ResearchMethodology_Project_Final.ipynb
* Remeber that every function requires amount of time to complete the process so.
* Following is the list of sequence of functions which are required to run after running the import libraries code cell section:
* Universal python decorator function to calculate total time.
```
def calc_time(func)
```
* Data preprocessing functions
```
def ravdess_data()
def crema_data()
def tess_data()
def saveee_data()
def fetch_data()
```
* Data augmentation functions
```
def noise(data)
def stretch(data, rate=0.8)
def shift(data)
def pitch(data, sampling_rate, pitch_factor=0.7)
```
* Below given functions are for feature extraction, run this functions only once as it requires time to extract features form auido. Also features extraction is a one time process. Once features are extracted we can carry out further processing and train the emotion recognition model.
```
def extract_features(data,sample_rate)
def get_features(path)
def Audio_features_extract()
```
* function to plot loss and accuracy curves
```
def plotgraph(history)
```
* Function to perform additional preprocessing on data and splitting the datasets.
```
def additional_preprocess(filepath)
def audio_features_final()
```
* function to build the emotion recognition model
```
def emotion_recognition_model(x_train,y_train,x_val,y_val)
```
* Run the full javascript template starting with
```
#this javascript is used to tell colab cell to open microphone and record audio
AUDIO_HTML = """
<script>
```
* function to invoke microphone of user and record audio
```
def get_audio()
```
* function for getting input speech features and real time testing
```
def get_features_recorded(data,sr)
def test_realtime(encoder)
```
* function to evaluate the performance of the model
```
def evaluate_model(x_train, x_test, y_train, y_test, x_val, y_val)
```
* main() function calls the functions in a sequence and after the execution of the main() function the deepl learning model for emotion recognition is ready.
```
@calc_time
def main():
#get train,test data and labels
x_train, x_test, y_train, y_test, x_val, y_val, encoder = audio_features_final()
#call the emotion recognition model
emotion_recognition_model(x_train,y_train,x_val,y_val)
#evaluate the model performance
evaluate_model(x_train, x_test, y_train, y_test, x_val, y_val)
if __name__:main()
```
* Once the model is trained and model file is generated one can use the below fucntions to test the model in real time enviourment.
```
x_train, x_test, y_train, y_test, x_val, y_val, encoder = audio_features_final()
test_realtime(encoder)
```
* If some one wants to used my trained model file directly then no need to run the main() function just run the above given two function to test in the real time enviourment.
* Also if using google colab make sure the function which are called inside the audio_features_final() and realtime_tested() are executed in advance as these two functions are dependent on them.
* Make sure all functions are called properly as mentioned in my colab file 1130532_ResearchMethodology_Project_Final.ipynb
* Additionally for my research work I carried on unkown sample data in different languages.
* So you can do if you want test on unkown samples by downloading additional data from this link "https://superkogito.github.io/SER-datasets/"
* You will require to preprocess the data then you can use my get_features_recorded(audio,sr) function to get the audio features then pass the audio features to the<p align="justify"> model to predict the outcome.
* I have already downloaded the few audio samples for testing on different voices and data is available on my google drive link, please sendme mail to access the data I will give acccess to the google dr</p>
* For my custom data in different languages I have used below functions to test the emotion recognition models.
```
def unknown_audio()
def diff_lang_test()
'''
* Whenever using the code make sure the function used inside the fnctions are called prior to executing the required function and all functions are executed in a proper sequence.
# Video on Installation / Usage
<p align="justify">Below given is the video on installation and usage of my project code in colab file. The video guides you by showing where to make path changes and how to install and run the code and test the code. The datasets link to download are already given above. I would request to read the installation and usage section and then watch this video so it will give a clear idea of the whole project and its working. click on the below youtube image to launch the video.</p>
<a href="https://youtu.be/kjttI89pIrI"><img src="https://img.shields.io/badge/youtube-red?style=flat&logo=youtube&labelColor=red"></a>
# Support / Contact details
Given below are few of my social media accounts where anyone can contact me.<br>
<a href="https://in.linkedin.com/in/devansh-mody-5013aaab"><img src="https://img.shields.io/badge/LinkedIn-blue?style=flat&logo=linkedin&labelColor=blue"></a>
<a href="https://mobile.twitter.com/modydevansh"><img src="https://img.shields.io/badge/twitter-blue?style=flat&logo=twitter&labelColor=blue"></a>
<a href="https://www.youtube.com/channel/UCtc_46TMSXPUMpzVP0IAJUw"><img src="https://img.shields.io/badge/youtube-red?style=flat&logo=youtube&labelColor=red"></a>
<a href="https://www.instagram.com/devansh_mody/?hl=en"><img src="https://img.shields.io/badge/instagram-purple?style=flat&logo=instagram&labelColor=pink"></a>
<a href="https://devanshmody.blogspot.com/"><img src="https://img.shields.io/badge/My bloging website-yellow?style=flat&logo=blog&labelColor=lightyellow"></a>
<br>One can also contact me by email <img src="https://img.shields.io/badge/gmail%20id-devanshmody2017%40gmail.com-red"><br>
For access to my google drive to see the setup of the whole project mail me on gmail id mentioned above access will be given to the selected people for some amount of time.
# Road-map (future ideas)
<p align="justify">The backgorund noise may cause errors when testing the model in real time enviourment and thus it can affect the output of the model. To avoid the noise audio segmentation needs to be performed, so I am planning to develop an audio segmentation model which can seprate user speech from background noise so emotions can be predicted accurately. Also I will be collecting audio in different formats extract features and train the model so a universal model can be developed. Once audio model is build it can be applied to video also by combining audio model of emotion recognition with facial model for emotion recognition, this can help in acheving more accurate output. Additionally three models can be combined that is textual, voice and facial based but it requires huge computation power and there is very limited study available on combining three models for emotion recogniton, beaucse a avoting mechanism or strategy needs to be developed for predicting the emotion from three models as there can be cases where each model can predict different emotions or two model predict same emotion and one predicts another emotion. Moreover I would like to build a audionet kind of embeddings similar to imagenet and word embeddings which will help other researchers working in this area to use pretrained audio embeddings.</p>
# How to contribute
<p align="justify">One can contribute by extracting features from different auido files the code for extracting features can be used from my ipynb file, different dataset may reqire different data preprocessing so one also write a function for data preprocessing and send me both prerporcessing code and csv file, so I can integrate both data preprocessing function and csv file with my csv file Audio_features_All_pr.csv. Additionally I am planning to build three model audio segmentation model, facial emotion recognition model and textual model so one can contribute by writing the function for the same and integrate it. Send me a git merge request to integrate code or contact me so we can check the integrity of code and combine the code. One can also branch out create their own branch and then we can merge the branch. Additionaly one can also fork the repository.</p>
# Google drive links
* Model file link: https://drive.google.com/file/d/1dGCxq08cyNYO86u_XePg7tQzerrjdEFM/view?usp=share_link
* Preprocessed dataset link: https://drive.google.com/file/d/17zqTlW2xqUJy1NA3fOBdXo43Ace4DruR/view?usp=share_link
# Authors / Acknowledgements
I would like to thank [@Ricardo]( https://ricardodeazambuja.com/deep_learning/2019/03/09/audio_and_video_google_colab/) for providing javascript code to inovke mircophone of user from google colab cell. As google colab dosent support audio recording using microphone so a javacript function needs to be written to inovke microphone and record auido. I would also like to thank [@Fadi Badine](https://keras.io/examples/audio/speaker_recognition_using_cnn/) my deep learning neural network model for emotion recognition is based on his model for automatic speech recognition.</p>
# References
[1] Francesc Alı́as, Joan Claudi Socoró and Xavier Sevillano, ”A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds”, Appl. Sci. 2016.<br>
[2] Kannan Venkataramanan and Haresh Rengaraj Rajamohan, ”Emotion Recognition from Speech”, arXiv:1912.10458v1 [cs.SD] 22 Dec 2019.<br>
[3] Haiyang Xu, Hui Zhang, Kun Han, Yun Wang, Yiping Peng and Xian-gang Li, ”Learning Alignment for Multimodal Emotion Recognition from Speech”, arXiv:1909.05645v2 [cs.CL] 3 Apr 2020.<br>
[4] Aharon Satt, Shai Rozenberg and Ron Hoory, ”Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms”, INTERSPEECH 2017, Stockholm, Sweden, August 20–24, 2017.<br>
[5] Jia Rong, Gang Li and Yi Ping Phoebe Chen, ”Acoustic feature selection for automatic emotion recognition from speech”, Information Processing and Management 45 (2009) 315–328.<br>
[6] K. Sreenivasa Rao, Tummala Pavan Kumar, Kusam Anusha, Bathina Leela, Ingilela Bhavana and Singavarapu V.S.K. Gowtham, ”Emotion Recognition from Speech”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (2) , 2012,3603-3607.<br>
[7] Vladimir Chernykh and Pavel Prikhodko, ”Emotion Recognition From Speech With Recurrent Neural Networks”, arXiv:1701.08071v2 [cs.CL] 5 Jul 2018.<br>
[8] Sabur Ajibola Alim and Nahrul Khair Alang Rashid, ”Some Commonly Speech Feature Feature Extraction Algorithms”. Published: December 12 2018, DOI: 10.5772/intechopen.80419.<br>
[9] Oh Wook Kwon, Kwokleung Chan, Jiucang Hao and Te Won Lee, ”Emotion Recognition by Speech Signals”, GENEVA, EUROSPEECH 2003.<br>
[10] K.V.Krishna Kishore and P.Krishna Satish, ”Emotion Recognition in Speech Using MFCC and Wavelet Features”, IEEE International Advance Computing Conference (IACC), 2013.<br>
[11] Panagiotis Tzirakis, Jiehao Zhang and Björn W. Schuller, ”END-TO-END SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORKS”, IEEE International Advance Computing Conference (IACC), 2018.<br>
# License
[](https://www.gnu.org/licenses/gpl-3.0)
# Project Status
<p align="justify">Emotion recognition model is finished and its ready and can be used in real time.
The 1130532_ResearchMethodology_Project_Final.ipynb file can be downloaded and used by providing neccesary path changes as mentioned in installation and usage sections.
I am looking forward to develop other models mentioned in road-map (future ideas) and integrate all those models with my current emotion recognition model.</p> |
Natkituwu/Kunokukulemonchini-7b-8.0bpw-exl2 | Natkituwu | 2024-03-19T17:14:14Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"alpaca",
"conversational",
"base_model:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:merge:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:grimjim/kukulemon-7B",
"base_model:merge:grimjim/kukulemon-7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T16:52:17Z | ---
base_model:
- grimjim/kukulemon-7B
- Nitral-AI/Kunocchini-7b-128k-test
library_name: transformers
tags:
- mergekit
- merge
- mistral
- alpaca
license: cc-by-nc-4.0
---
# Kunokukulemonchini-7b-8.0bpw-exl2
This is an 8.0 bpw exl2 quant of a merger [icefog72/Kunokukulemonchini-7b](https://huggingface.co/icefog72/Kunokukulemonchini-7b).
Near lossless quality. great to run if you have the resources to.
## Merge Details
Slightly edited kukulemon-7B config.json before merge to get at least ~32k context window.
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [grimjim/grimjim/kukulemon-7B](https://huggingface.co/grimjim/kukulemon-7B)
* [Nitral-AI/Kunocchini-7b-128k-test](https://huggingface.co/Nitral-AI/Kunocchini-7b-128k-test)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: grimjim/kukulemon-7B
layer_range: [0, 32]
- model: Nitral-AI/Kunocchini-7b-128k-test
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Kunocchini-7b-128k-test
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
``` |
4TB-USTC/nlp_sc_based_on_bert | 4TB-USTC | 2024-03-19T17:12:40Z | 111 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T16:34:55Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: nlp_sc_based_on_bert
results: []
---
<!-- 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. -->
# nlp_sc_based_on_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2481
- Accuracy: 0.8333
- F1: 0.8840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.4862 | 0.7819 | 0.8616 |
| 0.5416 | 2.0 | 918 | 0.5299 | 0.8480 | 0.8942 |
| 0.3661 | 3.0 | 1377 | 0.6462 | 0.8431 | 0.8904 |
| 0.2027 | 4.0 | 1836 | 0.7761 | 0.8431 | 0.8923 |
| 0.1227 | 5.0 | 2295 | 0.9341 | 0.8554 | 0.9002 |
| 0.0486 | 6.0 | 2754 | 1.0655 | 0.8382 | 0.8850 |
| 0.029 | 7.0 | 3213 | 1.2886 | 0.8284 | 0.8833 |
| 0.0281 | 8.0 | 3672 | 1.2164 | 0.8431 | 0.8937 |
| 0.0109 | 9.0 | 4131 | 1.2515 | 0.8407 | 0.8904 |
| 0.0049 | 10.0 | 4590 | 1.2481 | 0.8333 | 0.8840 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Trituenhantao/Taxi-v3 | Trituenhantao | 2024-03-19T17:11:58Z | 0 | 1 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-19T17:01:48Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Trituenhantao/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ai-bites/gemma-2b-ft-v1 | ai-bites | 2024-03-19T17:11:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T17:11:39Z | ---
library_name: transformers
tags: []
---
# 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]
|
hbacard/NeuralBeagle14-French-Aplaca | hbacard | 2024-03-19T17:10:32Z | 6 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/NeuralBeagle14-7B",
"jpacifico/French-Alpaca-7B-Instruct-beta",
"conversational",
"base_model:jpacifico/French-Alpaca-7B-Instruct-beta",
"base_model:merge:jpacifico/French-Alpaca-7B-Instruct-beta",
"base_model:mlabonne/NeuralBeagle14-7B",
"base_model:merge:mlabonne/NeuralBeagle14-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T17:06:49Z | ---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralBeagle14-7B
- jpacifico/French-Alpaca-7B-Instruct-beta
base_model:
- mlabonne/NeuralBeagle14-7B
- jpacifico/French-Alpaca-7B-Instruct-beta
---
# NeuralBeagle14-French-Aplaca
NeuralBeagle14-French-Aplaca is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
* [jpacifico/French-Alpaca-7B-Instruct-beta](https://huggingface.co/jpacifico/French-Alpaca-7B-Instruct-beta)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mlabonne/NeuralBeagle14-7B
layer_range: [0, 32]
- model: jpacifico/French-Alpaca-7B-Instruct-beta
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/NeuralBeagle14-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "hbacard/NeuralBeagle14-French-Aplaca"
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"])
``` |
alfassy/laso | alfassy | 2024-03-19T16:59:27Z | 0 | 0 | null | [
"arxiv:1902.09811",
"license:apache-2.0",
"region:us"
] | null | 2024-03-17T11:00:20Z | ---
license: apache-2.0
---
This are the models files for paper: LaSO: Label-Set Operations networks for multi-label few-shot learning
https://arxiv.org/abs/1902.09811
Code is available at: https://github.com/leokarlin/LaSO
The models in this repo does not support Huggin Face models class and can only be used with the code above. |
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_16_64_0.01_2_0.0002 | ferrazzipietro | 2024-03-19T16:48:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T16:42:39Z | ---
library_name: transformers
tags: []
---
# 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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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#### Metrics
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#### Summary
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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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|
karthiksab/new_summary_model | karthiksab | 2024-03-19T16:42:25Z | 109 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-19T01:22:49Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: new_summary_model
results: []
---
<!-- 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. -->
# new_summary_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4928
- Rouge1: 0.22
- Rouge2: 0.09
- Rougel: 0.18
- Rougelsum: 0.18
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.7131 | 1.0 | 1615 | 1.5056 | 0.21 | 0.09 | 0.18 | 0.18 |
| 1.7014 | 2.0 | 3230 | 1.4948 | 0.21 | 0.09 | 0.18 | 0.18 |
| 1.6827 | 3.0 | 4845 | 1.4928 | 0.22 | 0.09 | 0.18 | 0.18 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_8_torch.bfloat16_16_32_0.01_4_0.0002 | ferrazzipietro | 2024-03-19T16:41:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T16:41:02Z | ---
library_name: transformers
tags: []
---
# 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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- **Shared by [optional]:** [More Information Needed]
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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]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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#### Testing Data
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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]
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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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|
sarthakharne/bert-base-85-ep-pretrain-on-textbooks | sarthakharne | 2024-03-19T16:34:39Z | 194 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-03-19T16:32:56Z | ---
library_name: transformers
tags: []
---
# 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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- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
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[More Information Needed]
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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
### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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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]
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## Model Card Contact
[More Information Needed] |
vjprav33n/mT0_base_1e_30p_0064_v1 | vjprav33n | 2024-03-19T16:30:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T16:30:14Z | ---
library_name: transformers
tags: []
---
# 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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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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).
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maldv/winter-garden-7b-gamma | maldv | 2024-03-19T16:29:57Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"conversational",
"multi-task",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T02:01:26Z | ---
license: cc-by-nc-4.0
tags:
- merge
- conversational
- multi-task
pipeline_tag: text-generation
---
# Winter Garden 7B - Γ
It was mentioned that we are in the open ai dark winter; so I thought I would make myself a nice winter garden.
## An experiment
This time I did something a bit different.
* Mistral-7B-v0.1
and merged in
* Yarn-Mistral-7b-128k
* Thespis-Balanced-7b-v1
* ZySec-7B-v1
* LemonadeRP-4.5.3
* Noromaid-7B-0.4-DPO
* Prima-LelantaclesV6-7b
* West-Hermes-7B
* Capricorn-7B-DPO
* kun-kunoichi-v1-DPO-v2-SLERP-7B
* Kunoichi-DPO-v2-7B
* WestLake-7B-v2-laser-truthy-dpo
* StrangeMerges_6-7B-dare_ties
* NeuralMarcoro14-7B
* multi_verse_model
* Multi-Verse-RP-7B
* MonarchLake-7B
* AlphaMonarch-7B
in an iterative DARE-TIES tree merge, ordering the merge order by tensor-relative cosine similarity until the merge branches resolve to a single value.
## Chat Template
Basic Mistral `<s>[INST][/INST]` works pretty well. It seems smart, but we will see.
## Scores
Metric | Score
---|---
Average |
ARC |
HellaSwag |
MMLU |
TruthfulQA |
Winogrande |
GSM8K | |
ardneebwar/mistral_7b_mcq_generator | ardneebwar | 2024-03-19T16:29:15Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:ardneebwar/medmcqa-and-race",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-11T16:52:13Z | ---
library_name: transformers
license: apache-2.0
datasets:
- ardneebwar/medmcqa-and-race
language:
- en
pipeline_tag: text-generation
---
# Mistral 7B MCQ Generator
## Model Description
This model, named Mistral 7B MCQ Generator, is a fine-tuned version of the Mistralai/Mistral-7B-v0.1 aimed at generating multiple-choice questions (MCQs) with their correct answers. Developed to aid in educational content creation, this tool is perfect for educators, e-learning content creators, and students preparing for exams. The model was fine-tuned on a combination of medical MCQs and RACE dataset, ensuring a diverse range of topics and complexities in question generation.
## Intended Use
This model is intended for educational purposes, particularly for generating MCQs for studying, teaching, or content creation in the educational domain. It is designed to help in the preparation of quizzes, tests, and learning materials.
## Training Data
The model was trained on a custom dataset derived from the ardneebwar/medmcqa-and-race dataset available on Hugging Face. This dataset combines medical MCQs and reading comprehension questions from various educational levels, which were cleaned and preprocessed to suit the needs of MCQ generation.
## Training Procedure
The training was performed on a suitable environment supporting the demands of the Mistral 7B model. Specific settings included a learning rate of 2e-4, a batch size of 4, and a total of 3 epochs. The model underwent evaluation steps every 700 steps and used techniques like gradient accumulation and LoRA for efficient training.
## How to Use
You can utilize this model directly in your Kaggle notebooks or Jupyter notebooks as follows:
```python
import re
import torch
from transformers import pipeline, logging
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer, BitsAndBytesConfig, logging
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model_name = "mistralai/Mistral-7B-v0.1"
adapters_name = "ardneebwar/mistral_7b_mcq_generator"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
m = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
)
m = PeftModel.from_pretrained(m, adapters_name)
m = m.merge_and_unload()
tok = AutoTokenizer.from_pretrained(model_name)
tok.bos_token_id = 1
stop_token_ids = [0]
logging.set_verbosity(logging.CRITICAL)
def extract_mcqs(generated_text):
# This pattern looks for segments structured as MCQs based on your example
# It captures text following 'question:' up to an 'answer: X' format where X is A, B, C, or D
pattern = re.compile(r"question: (.*?) \| options: (.*?) \| answer: ([ABCD])", re.DOTALL)
# Find all matches in the generated text
matches = pattern.findall(generated_text)
unique_mcqs = set() # Using a set to avoid duplicates
mcqs = []
for match in matches:
question, options, answer = match
# Construct the MCQ string
mcq_text = f"question: {question.strip()} | options: {options.strip()} | answer: {answer.strip()}"
# Check for uniqueness before adding
if mcq_text not in unique_mcqs:
unique_mcqs.add(mcq_text)
mcqs.append(mcq_text)
return mcqs
# replace context with your own context.
prompt = "context: The Robot Operating System (ROS) is a set of software libraries and tools that help you build robot applications."
pipe = pipeline(task="text-generation", model=m, tokenizer=tok, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
generated_text = result[0]['generated_text']
unique_mcq = extract_mcqs(generated_text)
print(mcq)
```
## Limitations and Biases
The model's performance is subject to the quality and diversity of the training data. While it has been trained on a dataset that includes a range of topics, it may exhibit biases present in the training material. Users are advised to review the generated questions and answers for potential biases before use.
## References and Acknowledgments
This model was built using resources from the Hugging Face and PyTorch communities. Special thanks to the authors and contributors of the ardneebwar/medmcqa-and-race dataset and the Mistral 7B model.
## License
This model is open-sourced under the Apache 2.0 license. |
tzartrooper/medical_tracker_chatbot | tzartrooper | 2024-03-19T16:26:59Z | 4 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-18T17:33:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: distilbert/distilgpt2
model-index:
- name: medical_tracker_chatbot
results: []
---
<!-- 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. -->
# medical_tracker_chatbot
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8066
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0875 | 1.0 | 3977 | 1.9245 |
| 1.9299 | 2.0 | 7954 | 1.8280 |
| 1.906 | 3.0 | 11931 | 1.8066 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Qwen1.5-7B-Chat__adapters_en.layer1_8_torch.bfloat16_16_32_0.01_2_0.0002 | ferrazzipietro | 2024-03-19T16:26:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-11T16:21:05Z | ---
library_name: transformers
tags: []
---
# 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]
|
Shakhovak/flan-t5-base-absa-rest | Shakhovak | 2024-03-19T16:24:39Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-16T17:49:00Z | ---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-absa-rest
results: []
---
<!-- 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. -->
# flan-t5-base-absa-rest
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2490
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7338 | 0.45 | 200 | 0.3705 |
| 0.4162 | 0.9 | 400 | 0.2968 |
| 0.3228 | 1.35 | 600 | 0.2798 |
| 0.2954 | 1.8 | 800 | 0.2653 |
| 0.2679 | 2.25 | 1000 | 0.2483 |
| 0.2449 | 2.7 | 1200 | 0.2490 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
dokyoungkim/wmt19-finetuned-law-de-to-en | dokyoungkim | 2024-03-19T16:23:02Z | 92 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"fsmt",
"text2text-generation",
"tanslation",
"generated_from_trainer",
"base_model:facebook/wmt19-de-en",
"base_model:finetune:facebook/wmt19-de-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-19T04:34:04Z | ---
license: apache-2.0
base_model: facebook/wmt19-de-en
tags:
- tanslation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: wmt19-finetuned-law-de-to-en
results: []
---
<!-- 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. -->
# wmt19-finetuned-law-de-to-en
This model is a fine-tuned version of [facebook/wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2388
- Bleu: 56.9297
## 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: 40
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
|
minhah/vivit-b-16x2-kinetics400-finetuned-ucf101-subset | minhah | 2024-03-19T16:21:50Z | 62 | 0 | transformers | [
"transformers",
"safetensors",
"vivit",
"video-classification",
"generated_from_trainer",
"base_model:google/vivit-b-16x2-kinetics400",
"base_model:finetune:google/vivit-b-16x2-kinetics400",
"license:mit",
"endpoints_compatible",
"region:us"
] | video-classification | 2024-03-19T14:29:57Z | ---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-finetuned-ucf101-subset
results: []
---
<!-- 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. -->
# vivit-b-16x2-kinetics400-finetuned-ucf101-subset
This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2172
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 148
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7382 | 0.26 | 38 | 0.2207 | 1.0 |
| 0.0404 | 1.26 | 76 | 0.0549 | 0.9730 |
| 0.0115 | 2.26 | 114 | 0.0282 | 1.0 |
| 0.0172 | 3.23 | 148 | 0.0236 | 1.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Natkituwu/Kunokukulemonchini-7b-5.0bpw-exl2 | Natkituwu | 2024-03-19T16:21:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"alpaca",
"conversational",
"base_model:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:merge:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:grimjim/kukulemon-7B",
"base_model:merge:grimjim/kukulemon-7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T15:10:54Z | ---
base_model:
- grimjim/kukulemon-7B
- Nitral-AI/Kunocchini-7b-128k-test
library_name: transformers
tags:
- mergekit
- merge
- mistral
- alpaca
license: cc-by-nc-4.0
---
# Kunokukulemonchini-7b-5.0bpw-exl2
This is an 5.0 bpw exl2 quant of a merger [icefog72/Kunokukulemonchini-7b](https://huggingface.co/icefog72/Kunokukulemonchini-7b).
Good balance between 4.1bpw and 6.5bpw, should give more context than 6.5bpw.
## Merge Details
Slightly edited kukulemon-7B config.json before merge to get at least ~32k context window.
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [grimjim/grimjim/kukulemon-7B](https://huggingface.co/grimjim/kukulemon-7B)
* [Nitral-AI/Kunocchini-7b-128k-test](https://huggingface.co/Nitral-AI/Kunocchini-7b-128k-test)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: grimjim/kukulemon-7B
layer_range: [0, 32]
- model: Nitral-AI/Kunocchini-7b-128k-test
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Kunocchini-7b-128k-test
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
``` |
mieszkok/shi-labs_oneformer_ade20k_swin_large_geopose3k_original_images900_epochs5 | mieszkok | 2024-03-19T16:19:15Z | 52 | 0 | transformers | [
"transformers",
"safetensors",
"oneformer",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T16:17:52Z | ---
library_name: transformers
tags: []
---
# 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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<!-- 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
<!-- 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Natkituwu/Kunokukulemonchini-7b-3.5bpw-exl2 | Natkituwu | 2024-03-19T16:18:36Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"alpaca",
"conversational",
"base_model:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:merge:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:grimjim/kukulemon-7B",
"base_model:merge:grimjim/kukulemon-7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T16:05:10Z | ---
base_model:
- grimjim/kukulemon-7B
- Nitral-AI/Kunocchini-7b-128k-test
library_name: transformers
tags:
- mergekit
- merge
- mistral
- alpaca
license: cc-by-nc-4.0
---
# Kunokukulemonchini-7b-3.5bpw-exl2
This is an 3.5 bpw exl2 quant of a merger [icefog72/Kunokukulemonchini-7b](https://huggingface.co/icefog72/Kunokukulemonchini-7b).
Only use if you have low end hardware. Use 4.1bpw, 5.0, and 6.5 versions instead if you have better hardware.
## Merge Details
Slightly edited kukulemon-7B config.json before merge to get at least ~32k context window.
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [grimjim/grimjim/kukulemon-7B](https://huggingface.co/grimjim/kukulemon-7B)
* [Nitral-AI/Kunocchini-7b-128k-test](https://huggingface.co/Nitral-AI/Kunocchini-7b-128k-test)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: grimjim/kukulemon-7B
layer_range: [0, 32]
- model: Nitral-AI/Kunocchini-7b-128k-test
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Kunocchini-7b-128k-test
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
``` |
dewaalmatin/gc7_model | dewaalmatin | 2024-03-19T16:17:11Z | 1 | 0 | keras | [
"keras",
"arxiv:1910.09700",
"region:us"
] | null | 2024-03-19T14:25:35Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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] |
sirjosephenry/finetuning-emotion-model | sirjosephenry | 2024-03-19T16:07:26Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T15:31:10Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: finetuning-emotion-model
results: []
---
<!-- 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. -->
# finetuning-emotion-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
prampampam/light_peft_model | prampampam | 2024-03-19T16:07:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T16:07:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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] |
csupreme19/gemma-ko-ft-7b-test | csupreme19 | 2024-03-19T16:05:56Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T15:58:40Z | ---
library_name: transformers
tags: []
---
# 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]
|
cogbuji/OpenHermes-2.5-Mistral-7B-mlx-4bit | cogbuji | 2024-03-19T16:03:45Z | 14 | 1 | mlx | [
"mlx",
"safetensors",
"mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-03-19T02:30:11Z | ---
language:
- en
license: apache-2.0
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- mlx
datasets:
- teknium/OpenHermes-2.5
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: OpenHermes-2-Mistral-7B
results: []
---
# cogbuji/OpenHermes-2.5-Mistral-7B-mlx-4bit
This model was converted to MLX format from [teknium/OpenHermes-2.5-Mistral-7B](/teknium/OpenHermes-2.5-Mistral-7B) and quantized.
Refer to the [original model card](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) for more details on the model.
It was converted and quantized with mlx **0.7.0** and mlx_lm **0.3.0** and should be used with those versions. Later versions of these may deprecate this model
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cogbuji/OpenHermes-2.5-Mistral-7B-mlx-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
bartowski/Mistral-Plus-7B-exl2 | bartowski | 2024-03-19T16:03:09Z | 0 | 1 | null | [
"text-generation",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-03-19T15:51:18Z | ---
license: apache-2.0
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Mistral-Plus-7B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/zhengchenphd/Mistral-Plus-7B
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Mistral-Plus-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Mistral-Plus-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Mistral-Plus-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Mistral-Plus-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Mistral-Plus-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Mistral-Plus-7B-exl2 Mistral-Plus-7B-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Mistral-Plus-7B-exl2`:
```shell
mkdir Mistral-Plus-7B-exl2
huggingface-cli download bartowski/Mistral-Plus-7B-exl2 --local-dir Mistral-Plus-7B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Mistral-Plus-7B-exl2-6_5
huggingface-cli download bartowski/Mistral-Plus-7B-exl2 --revision 6_5 --local-dir Mistral-Plus-7B-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Mistral-Plus-7B-exl2-6.5
huggingface-cli download bartowski/Mistral-Plus-7B-exl2 --revision 6_5 --local-dir Mistral-Plus-7B-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
helloyeew/distilbert-base-uncased-finetuned-emotion | helloyeew | 2024-03-19T15:54:14Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-19T15:39:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9253464175347648
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2266
- Accuracy: 0.9255
- F1: 0.9253
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8402 | 1.0 | 250 | 0.3315 | 0.9035 | 0.9008 |
| 0.26 | 2.0 | 500 | 0.2266 | 0.9255 | 0.9253 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.2.1
- Datasets 1.16.1
- Tokenizers 0.15.2
|
prampampam/light-sdxl-lora | prampampam | 2024-03-19T15:53:33Z | 6 | 1 | diffusers | [
"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",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-03-19T14:50:41Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- diffusers-training
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_0.png"
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_1.png"
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_2.png"
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - prampampam/light-sdxl-lora
<Gallery />
## Model description
### These are prampampam/light-sdxl-lora 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 **[`light-sdxl-lora.safetensors` here 💾](/prampampam/light-sdxl-lora/blob/main/light-sdxl-lora.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:light-sdxl-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`light-sdxl-lora_emb.safetensors` here 💾](/prampampam/light-sdxl-lora/blob/main/light-sdxl-lora_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `light-sdxl-lora_emb` to your prompt. For example, `a photo of light-sdxl-lora_emb`
(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('prampampam/light-sdxl-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='prampampam/light-sdxl-lora', filename='light-sdxl-lora_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('a photo of living room with <s0><s1>').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 `VIBIAARRAY` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/prampampam/light-sdxl-lora/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.
|
sarthakharne/bert-base-80-ep-pretrain-on-textbooks | sarthakharne | 2024-03-19T15:50:54Z | 196 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-03-19T15:49:16Z | ---
library_name: transformers
tags: []
---
# 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] |
stablediffusionapi/epicrealism-xl | stablediffusionapi | 2024-03-19T15:49:03Z | 1,359 | 5 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-03-19T15:46:48Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "epicrealism-xl"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/epicrealism-xl)
Model link: [View model](https://modelslab.com/models/epicrealism-xl)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "epicrealism-xl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** |
jvh/whisper-medium-quant-ct2 | jvh | 2024-03-19T15:47:24Z | 14 | 2 | ctranslate2 | [
"ctranslate2",
"audio",
"automatic-speech-recognition",
"int8",
"quanto",
"faster-whisper",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"yue",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2024-03-19T14:34:56Z | ---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
- yue
tags:
- audio
- automatic-speech-recognition
- int8
- quanto
- faster-whisper
license: apache-2.0
library_name: ctranslate2
---
# Model Card for Model ID
This model is quantized using the [Quanto Python Package](https://github.com/huggingface/quanto) and the [CTranslate2 Python Package](https://github.com/OpenNMT/CTranslate2).
From my early tests:
- Much less GPU memory required
- It seems that performance is on par with the original
- It seems that this combination is faster than just using the CTranslate2 int8 quantization.
Quantization method TBA.
To use this model, use the faster_whisper module as stated in [the original faster-whisper model](https://huggingface.co/Systran/faster-whisper-large-v3)
Or use [WhisperX](https://github.com/m-bain/whisperX), this is what I used for my small tests (do not forget to set dtype to int8).
Any benchmark results are appreciated. I probably do not have time to do it myself.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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] |
jvh/whisper-large-v2-quant-ct2 | jvh | 2024-03-19T15:46:59Z | 11 | 3 | ctranslate2 | [
"ctranslate2",
"audio",
"automatic-speech-recognition",
"int8",
"quanto",
"faster-whisper",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"yue",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2024-03-19T14:32:17Z | ---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
- yue
tags:
- audio
- automatic-speech-recognition
- int8
- quanto
- faster-whisper
license: apache-2.0
library_name: ctranslate2
---
# Model Card for Model ID
This model is quantized using the [Quanto Python Package](https://github.com/huggingface/quanto) and the [CTranslate2 Python Package](https://github.com/OpenNMT/CTranslate2).
From my early tests:
- Much less GPU memory required
- It seems that performance is on par with the original
- It seems that this combination is faster than just using the CTranslate2 int8 quantization.
Quantization method TBA.
To use this model use the faster_whisper module as stated in [the original faster-whisper model](https://huggingface.co/Systran/faster-whisper-large-v3)
Or use [WhisperX](https://github.com/m-bain/whisperX), this is what I used for my small tests (do not forget to set dtype to int8).
Any benchmark results are appreciated. I probably do not have time to do it myself.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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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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[More Information Needed] |
jvh/whisper-base-quant-ct2 | jvh | 2024-03-19T15:46:35Z | 5 | 2 | ctranslate2 | [
"ctranslate2",
"audio",
"automatic-speech-recognition",
"int8",
"quanto",
"faster-whisper",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"yue",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | 2024-03-19T14:30:02Z | ---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
- yue
tags:
- audio
- automatic-speech-recognition
- int8
- quanto
- faster-whisper
license: apache-2.0
library_name: ctranslate2
---
# Model Card for Model ID
This model is quantized using the [Quanto Python Package](https://github.com/huggingface/quanto) and the [CTranslate2 Python Package](https://github.com/OpenNMT/CTranslate2).
From my early tests:
- Much less GPU memory required
- It seems that performance is on par with the original
- It seems that this combination is faster than just using the CTranslate2 int8 quantization.
Quantization method TBA.
To use this model use the faster_whisper module as stated in [the original faster-whisper model](https://huggingface.co/Systran/faster-whisper-large-v3)
Or use [WhisperX](https://github.com/m-bain/whisperX), this is what I used for my small tests (do not forget to set dtype to int8).
Any benchmark results are appreciated. I probably do not have time to do it myself.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
peldrak/mask2former-large-cityscapes-finetuned-coastTrain | peldrak | 2024-03-19T15:41:48Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"mask2former",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-18T01:58:37Z | ---
library_name: transformers
tags: []
---
# 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
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|
joosma/ppo-Huggy | joosma | 2024-03-19T15:40:26Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-03-19T15:40:20Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
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: joosma/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lbaeriswyl/dqn-SpaceInvadersNoFrameskip-v4 | lbaeriswyl | 2024-03-19T15:37:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-19T15:37:03Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 543.00 +/- 216.69
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lbaeriswyl -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lbaeriswyl -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lbaeriswyl
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
prampampam/sofa-sdxl-lora | prampampam | 2024-03-19T15:36:07Z | 5 | 1 | diffusers | [
"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",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-03-19T14:21:42Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- diffusers-training
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_0.png"
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_1.png"
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_2.png"
- text: 'a photo of living room with <s0><s1>'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - prampampam/sofa-sdxl-lora
<Gallery />
## Model description
### These are prampampam/sofa-sdxl-lora 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 **[`sofa-sdxl-lora.safetensors` here 💾](/prampampam/sofa-sdxl-lora/blob/main/sofa-sdxl-lora.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:sofa-sdxl-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`sofa-sdxl-lora_emb.safetensors` here 💾](/prampampam/sofa-sdxl-lora/blob/main/sofa-sdxl-lora_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `sofa-sdxl-lora_emb` to your prompt. For example, `a photo of sofa-sdxl-lora_emb`
(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('prampampam/sofa-sdxl-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='prampampam/sofa-sdxl-lora', filename='sofa-sdxl-lora_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('a photo of living room with <s0><s1>').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 `MYSOFA` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/prampampam/sofa-sdxl-lora/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.
|
peldrak/mask2former-large-ade-finetuned-coastTrain | peldrak | 2024-03-19T15:32:09Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"mask2former",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-18T02:01:21Z | ---
library_name: transformers
tags: []
---
# 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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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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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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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
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### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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|
kousw/stablelm-gamma-7b-chatvector | kousw | 2024-03-19T15:29:54Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"ja",
"arxiv:2310.04799",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T15:00:11Z | ---
license: apache-2.0
language:
- ja
---
This model employs the technique described in ["Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages"](https://arxiv.org/abs/2310.04799).
It is based on [stablelm-gamma-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b), a model that has not undergone instruction tuning, which was pre-trained using [mistral-7b-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
To extract chat vectors, mistral-7b-v0.1 was "subtracted" from [mistral-7b-instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
By applying these extracted chat vectors to the non-instruction-tuned model stablelm-gamma-7b, an effect equivalent to instruction tuning is achieved.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("kousw/stablelm-gamma-7b-chatvector")
tokenizer = AutoTokenizer.from_pretrained("kousw/stablelm-gamma-7b-chatvector")
messages = [
{"role": "user", "content": "与えられたことわざの意味を小学生でも分かるように教えてください。"},
{"role": "assistant", "content": "はい、どんなことわざでもわかりやすく答えます"},
{"role": "user", "content": "情けは人のためならず"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=256, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
|
alterf/det | alterf | 2024-03-19T15:28:24Z | 190 | 0 | transformers | [
"transformers",
"safetensors",
"table-transformer",
"image-feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-feature-extraction | 2024-03-19T15:28:15Z | ---
library_name: transformers
tags: []
---
# 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]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ighoshsubho/mistral-7b-oig-unsloth | ighoshsubho | 2024-03-19T15:24:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T15:24:47Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** ighoshsubho
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Liu-Xiang/Llama-2-7b-chat-hf-tuned-adapters | Liu-Xiang | 2024-03-19T15:24:57Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-02-25T02:42:46Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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
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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]
#### 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]
### Framework versions
- PEFT 0.9.1.dev0 |
ighoshsubho/mistral-7b-oig-unsloth-merged | ighoshsubho | 2024-03-19T15:24:16Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T15:17:24Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** ighoshsubho
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
adamjweintraut/bart-finetuned-lyrlen-512 | adamjweintraut | 2024-03-19T15:13:29Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large",
"base_model:finetune:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-19T05:12:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/bart-large
model-index:
- name: bart-finetuned-lyrlen-512
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-lyrlen-512
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7206
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.221 | 0.04 | 500 | 1.9667 |
| 2.0336 | 0.08 | 1000 | 1.8762 |
| 1.9563 | 0.12 | 1500 | 1.8565 |
| 1.9555 | 0.17 | 2000 | 1.8392 |
| 1.9072 | 0.21 | 2500 | 1.8214 |
| 1.8796 | 0.25 | 3000 | 1.8246 |
| 1.8955 | 0.29 | 3500 | 1.8050 |
| 1.8254 | 0.33 | 4000 | 1.8069 |
| 1.8518 | 0.38 | 4500 | 1.7873 |
| 1.8471 | 0.42 | 5000 | 1.7880 |
| 1.8536 | 0.46 | 5500 | 1.7736 |
| 1.8075 | 0.5 | 6000 | 1.7772 |
| 1.8143 | 0.54 | 6500 | 1.7724 |
| 1.8383 | 0.58 | 7000 | 1.7670 |
| 1.746 | 0.62 | 7500 | 1.7741 |
| 1.7844 | 0.67 | 8000 | 1.7608 |
| 1.7761 | 0.71 | 8500 | 1.7680 |
| 1.7367 | 0.75 | 9000 | 1.7555 |
| 1.7656 | 0.79 | 9500 | 1.7508 |
| 1.7467 | 0.83 | 10000 | 1.7558 |
| 1.7744 | 0.88 | 10500 | 1.7449 |
| 1.7513 | 0.92 | 11000 | 1.7462 |
| 1.7482 | 0.96 | 11500 | 1.7576 |
| 1.724 | 1.0 | 12000 | 1.7525 |
| 1.7043 | 1.04 | 12500 | 1.7746 |
| 1.6869 | 1.08 | 13000 | 1.7531 |
| 1.7405 | 1.12 | 13500 | 1.7473 |
| 1.7343 | 1.17 | 14000 | 1.7396 |
| 1.649 | 1.21 | 14500 | 1.7384 |
| 1.7208 | 1.25 | 15000 | 1.7368 |
| 1.6931 | 1.29 | 15500 | 1.7404 |
| 1.5941 | 1.33 | 16000 | 1.8223 |
| 1.6651 | 1.38 | 16500 | 1.7287 |
| 1.6649 | 1.42 | 17000 | 1.7413 |
| 1.7108 | 1.46 | 17500 | 1.7304 |
| 1.713 | 1.5 | 18000 | 1.7263 |
| 1.6866 | 1.54 | 18500 | 1.7139 |
| 1.6461 | 1.58 | 19000 | 1.7221 |
| 1.6886 | 1.62 | 19500 | 1.7159 |
| 1.6511 | 1.67 | 20000 | 1.7302 |
| 1.6626 | 1.71 | 20500 | 1.7182 |
| 1.7052 | 1.75 | 21000 | 1.7163 |
| 1.6831 | 1.79 | 21500 | 1.7168 |
| 1.6057 | 1.83 | 22000 | 1.7151 |
| 1.6761 | 1.88 | 22500 | 1.7117 |
| 1.6668 | 1.92 | 23000 | 1.7164 |
| 1.612 | 1.96 | 23500 | 1.7122 |
| 1.6617 | 2.0 | 24000 | 1.7131 |
| 1.641 | 2.04 | 24500 | 1.7277 |
| 1.6595 | 2.08 | 25000 | 1.7289 |
| 1.6723 | 2.12 | 25500 | 1.7192 |
| 1.6347 | 2.17 | 26000 | 1.7259 |
| 1.6684 | 2.21 | 26500 | 1.7211 |
| 1.6098 | 2.25 | 27000 | 1.7316 |
| 1.6025 | 2.29 | 27500 | 1.7213 |
| 1.5567 | 2.33 | 28000 | 1.7238 |
| 1.6564 | 2.38 | 28500 | 1.7185 |
| 1.7078 | 2.42 | 29000 | 1.7393 |
| 1.6308 | 2.46 | 29500 | 1.7234 |
| 1.6402 | 2.5 | 30000 | 1.7319 |
| 1.6333 | 2.54 | 30500 | 1.7197 |
| 1.6249 | 2.58 | 31000 | 1.7298 |
| 1.6366 | 2.62 | 31500 | 1.7235 |
| 1.6245 | 2.67 | 32000 | 1.7289 |
| 1.6044 | 2.71 | 32500 | 1.7160 |
| 1.6095 | 2.75 | 33000 | 1.7172 |
| 1.6621 | 2.79 | 33500 | 1.7210 |
| 1.6883 | 2.83 | 34000 | 1.7169 |
| 1.6449 | 2.88 | 34500 | 1.7155 |
| 1.6439 | 2.92 | 35000 | 1.7201 |
| 1.6358 | 2.96 | 35500 | 1.7188 |
| 1.6033 | 3.0 | 36000 | 1.7206 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.0.dev20230621+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ichetandhembre/chat_lora_unsloth_wikipedia_qa_train_4_bit_with_hint | ichetandhembre | 2024-03-19T15:10:09Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-19T15:04:36Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/mistral-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** ichetandhembre
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
santhoshkammari/output | santhoshkammari | 2024-03-19T15:08:42Z | 2 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/flan-t5-small",
"base_model:adapter:google/flan-t5-small",
"license:apache-2.0",
"region:us"
] | null | 2024-03-19T14:11:27Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/flan-t5-small
datasets:
- generator
model-index:
- name: FLANFt-t5-small
results: []
---
<!-- 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. -->
# FLANFt-t5-small
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) 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: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
arvnoodle/hcl-codellama-7b-instruct-javascript-lotuscript-GGUF | arvnoodle | 2024-03-19T15:02:18Z | 82 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:quantized:codellama/CodeLlama-7b-Instruct-hf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-03-19T15:00:10Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: codellama/CodeLlama-7b-Instruct-hf
---
# Uploaded model
- **Developed by:** arvnoodle
- **License:** apache-2.0
- **Finetuned from model :** codellama/CodeLlama-7b-Instruct-hf
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)
|
arcee-ai/Patent-Instruct-Extended | arcee-ai | 2024-03-19T15:02:06Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"arcee-ai/Patent-Instruct-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T14:54:37Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- arcee-ai/Patent-Instruct-7b
---
# Patent-Instruct-Extended
Patent-Instruct-Extended is an extension of the following models using the passthrough method and [mergekit](https://github.com/cg123/mergekit):
* [arcee-ai/Patent-Instruct-7b](https://huggingface.co/arcee-ai/Patent-Instruct-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 0
- 4
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 2
- 4
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 4
- 8
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 6
- 8
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 8
- 12
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 10
- 12
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 12
- 16
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 14
- 16
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 16
- 20
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 18
- 20
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 20
- 24
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 22
- 24
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 24
- 28
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 26
- 28
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 28
- 32
- sources:
- model: arcee-ai/Patent-Instruct-7b
layer_range:
- 30
- 32
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
merge_method: passthrough
dtype: bfloat16
``` |
Samuael/geez_30k_mt5 | Samuael | 2024-03-19T14:59:09Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-19T07:29:05Z | ---
library_name: transformers
tags: []
---
# 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]
|
hakankenar/gemma-2b-it-8bit-finetune-test-alpaca-changed | hakankenar | 2024-03-19T14:56:01Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T14:52:10Z | ---
library_name: transformers
tags: []
---
# 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
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|
mjacobo/my_awesome_billsum_model | mjacobo | 2024-03-19T14:54:35Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-13T19:31:32Z | ---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results: []
---
<!-- 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. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4521
- Rouge1: 0.1351
- Rouge2: 0.0486
- Rougel: 0.1102
- Rougelsum: 0.11
- Gen Len: 19.0
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.7471 | 0.129 | 0.041 | 0.1081 | 0.1078 | 19.0 |
| No log | 2.0 | 124 | 2.5302 | 0.1353 | 0.0485 | 0.113 | 0.1125 | 19.0 |
| No log | 3.0 | 186 | 2.4697 | 0.1359 | 0.0518 | 0.1118 | 0.1117 | 19.0 |
| No log | 4.0 | 248 | 2.4521 | 0.1351 | 0.0486 | 0.1102 | 0.11 | 19.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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