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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-09 06:31:45
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nlpzhaof/aligngpt-13b-pretrain
|
nlpzhaof
| 2024-06-29T03:59:36Z | 5 | 0 |
transformers
|
[
"transformers",
"aligngpt",
"text-generation",
"en",
"arxiv:2405.14129",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-23T05:39:40Z |
---
license: apache-2.0
language:
- en
---
# AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability
[[Project Page](https://aligngpt-vl.github.io/)] [[Paper](https://arxiv.org/abs/2405.14129)] [[Demo](http://47.116.173.89:7870/)] [[Model](https://huggingface.co/nlpzhaof)]
Authors: [Fei Zhao*](https://scholar.google.com/citations?user=V01xzWQAAAAJ&hl=zh-CN), Taotian Pang*, Chunhui Li, [Zhen Wu](https://scholar.google.com/citations?user=IoGlgtoAAAAJ&hl=zh-CN), Junjie Guo, Shangyu Xing, [Xinyu Dai](https://scholar.google.com/citations?user=zpWB1CgAAAAJ&hl=zh-CN)
## News and Updates
- [5/24] 🔥 We released **AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability**. Checkout the [paper](https://arxiv.org/abs/2405.14129) and [demo](http://47.116.173.89:7870/).
## Model Zoo
| Model | LLM | Vision Backbone | Pre-training | Instruct-tuning |
|----------|----------|-----------|---|---|
| AlignGPT-7B | [Vicuna 7B](https://huggingface.co/lmsys/vicuna-7b-v1.5) | [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14-336) |[aligngpt-7b-pretrain](https://huggingface.co/nlpzhaof/aligngpt-7b-pretrain/tree/main)| [aligngpt-7b](https://huggingface.co/nlpzhaof/aligngpt-7b/tree/main)|
| AlignGPT-13B | [Vicuna 13B](https://huggingface.co/lmsys/vicuna-13b-v1.5) | [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14-336) |[aligngpt-13b-pretrain](https://huggingface.co/nlpzhaof/aligngpt-13b-pretrain/tree/main)| [aligngpt-13b](https://huggingface.co/nlpzhaof/aligngpt-13b/tree/main)|
| AlignGPT-LLaMA2 | [LLaMA-2-7B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) | [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14-336) |To be released| To be released|
| AlignGPT-LLaMA3 | [LLaMA-3-8B-Base](https://huggingface.co/meta-llama/Meta-Llama-3-8B) | [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14-336) |To be released|To be released|
## Performance
| Model | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED | LLaVA-Bench-Wild | MM-Vet |
|----------|---|---|---|---|---|---|---|---|---|---|---|---|
| AlignGPT-7B | 79.1 | 62.9 | 54.2 | 68.5 | 58.4 | 86.0 | 1527.4 | 67.3 | 59.9 | 66.5 | 68.4 | 30.8 |
| AlignGPT-13B | 80.0 | 63.6 | 56.4 | 70.3 | 60.2 | 86.2 | 1572.0 | 69.5 | 63.7 | 67.8 | 75.2 | 35.6 |
## Citation
If you find AlignGPT useful for your research and applications, please cite using this BibTeX:
```
@misc{zhao2024aligngpt,
title={AlignGPT: Multi-modal Large Language Models with Adaptive Alignment Capability},
author={Fei Zhao and Taotian Pang and Chunhui Li and Zhen Wu and Junjie Guo and Shangyu Xing and Xinyu Dai},
year={2024},
eprint={2405.14129},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
[](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)[](https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE)
The data and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
|
netcat420/MFANN3bv0.14
|
netcat420
| 2024-06-29T03:27:13Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-29T03:19:02Z |
---
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]
- **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]
|
Koleshjr/mistral_7b_v2_q4_k_m_10_epochs
|
Koleshjr
| 2024-06-29T03:13:59Z | 12 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-29T03:03:49Z |
---
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
---
# Uploaded model
- **Developed by:** Koleshjr
- **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)
|
chainup244/google-gemma-2b-1719630584
|
chainup244
| 2024-06-29T03:12:10Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-29T03:09:46Z |
---
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]
|
chainup244/Qwen-Qwen1.5-0.5B-1719629973
|
chainup244
| 2024-06-29T03:00:05Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-29T02:59:34Z |
---
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]
|
John6666/cocoa-mix-xl-v3-sdxl
|
John6666
| 2024-06-29T02:48:12Z | 41 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-06-29T02:43:37Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
---
Original model is [here](https://civitai.com/models/530602/cocoamixxl?modelVersionId=605696).
|
youssefabdelmottaleb/Garbage-Classification-SWIN-Transformer
|
youssefabdelmottaleb
| 2024-06-29T02:48:01Z | 212 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-28T23:30:52Z |
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Garbage-Classification-SWIN-Transformer
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. -->
# Garbage-Classification-SWIN-Transformer
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0440
- Accuracy: 0.9900
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1969 | 0.9973 | 280 | 0.1740 | 0.9409 |
| 0.1014 | 1.9982 | 561 | 0.0752 | 0.9755 |
| 0.0333 | 2.9991 | 842 | 0.0551 | 0.9824 |
| 0.0332 | 4.0 | 1123 | 0.0526 | 0.9845 |
| 0.0218 | 4.9973 | 1403 | 0.0511 | 0.9866 |
| 0.0086 | 5.9982 | 1684 | 0.0515 | 0.9873 |
| 0.0057 | 6.9991 | 1965 | 0.0462 | 0.9875 |
| 0.0043 | 8.0 | 2246 | 0.0453 | 0.9891 |
| 0.0012 | 8.9973 | 2526 | 0.0460 | 0.9888 |
| 0.0017 | 9.9733 | 2800 | 0.0440 | 0.9900 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
Koleshjr/mistral_7b_v2_8bit_q8_0_10_epochs
|
Koleshjr
| 2024-06-29T02:43:36Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-29T02:39:26Z |
---
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
---
# Uploaded model
- **Developed by:** Koleshjr
- **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)
|
chainup244/Qwen-Qwen1.5-1.8B-1719628883
|
chainup244
| 2024-06-29T02:43:08Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-29T02:41:25Z |
---
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]
|
chainup244/Qwen-Qwen1.5-0.5B-1719628741
|
chainup244
| 2024-06-29T02:39:33Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-29T02:39:02Z |
---
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]
- **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]
|
12unique/animals
|
12unique
| 2024-06-29T02:35:02Z | 195 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-29T02:34:55Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: animals
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9821428656578064
---
# animals
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cat
#### cow
#### dog
#### horse
#### lion
|
Zelyanoth/traduction_fon_french
|
Zelyanoth
| 2024-06-29T02:18:18Z | 7 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:generator",
"base_model:google/madlad400-3b-mt",
"base_model:adapter:google/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | null | 2024-06-23T23:44:32Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google/madlad400-3b-mt
datasets:
- generator
metrics:
- bleu
model-index:
- name: traduction_fon_french
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. -->
# traduction_fon_french
This model is a fine-tuned version of [google/madlad400-3b-mt](https://huggingface.co/google/madlad400-3b-mt) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2051
- Bleu: 4.2618
- Gen Len: 7.1747
## 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: 0.00016
- train_batch_size: 18
- eval_batch_size: 18
- 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 | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.8931 | 1.0 | 4957 | 4.2563 | 4.206 | 7.2216 |
| 1.8748 | 2.0 | 9914 | 4.2353 | 4.3796 | 7.1632 |
| 1.9018 | 3.0 | 14871 | 4.2051 | 4.2618 | 7.1747 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
kmilesz/suzukii2
|
kmilesz
| 2024-06-29T01:57:30Z | 4 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"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
| 2024-06-29T01:57:28Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: A photo of suzukii
output:
url: image-0.png
- text: A photo of suzukii
output:
url: image-1.png
- text: A photo of suzukii
output:
url: image-2.png
- text: A photo of suzukii
output:
url: image-3.png
- text: A photo of suzukii
output:
url: image-4.png
- text: A photo of suzukii
output:
url: image-5.png
- text: A photo of suzukii
output:
url: image-6.png
- text: A photo of suzukii
output:
url: image-7.png
- text: A photo of suzukii
output:
url: image-8.png
- text: A photo of suzukii
output:
url: image-9.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - kmilesz/suzukii2
<Gallery />
## Model description
### These are kmilesz/suzukii2 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 **[`suzukii2.safetensors` here 💾](/kmilesz/suzukii2/blob/main/suzukii2.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:suzukii2:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`suzukii2_emb.safetensors` here 💾](/kmilesz/suzukii2/blob/main/suzukii2_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `suzukii2_emb` to your prompt. For example, `A photo of suzukii2_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('kmilesz/suzukii2', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='kmilesz/suzukii2', filename='suzukii2_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 <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 `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/kmilesz/suzukii2/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.
|
BigHuggyD/cohereforai_c4ai-command-r-plus_exl2_5.5bpw_h8
|
BigHuggyD
| 2024-06-29T01:56:19Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"cohere",
"text-generation",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"exl2",
"region:us"
] |
text-generation
| 2024-06-29T01:26:29Z |
---
inference: false
license: cc-by-nc-4.0
library_name: transformers
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
---
# Model Card for C4AI Command R+
🚨 **This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit)**.
## Model Summary
C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
C4AI Command R+ is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is [C4AI Command R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
Developed by: [Cohere](https://cohere.com/) and [Cohere For AI](https://cohere.for.ai)
- Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
- License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
- Model: c4ai-command-r-plus
- Model Size: 104 billion parameters
- Context length: 128K
**Try C4AI Command R+**
You can try out C4AI Command R+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus).
**Usage**
Please install `transformers` from the source repository that includes the necessary changes for this model.
```python
# pip install 'git+https://github.com/huggingface/transformers.git'
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
**Quantized model through bitsandbytes, 8-bit precision**
```python
# pip install 'git+https://github.com/huggingface/transformers.git' bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
**Quantized model through bitsandbytes, 4-bit precision**
This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit).
## Model Details
**Input**: Models input text only.
**Output**: Models generate text only.
**Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety.
**Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic.
Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.
**Context length**: Command R+ supports a context length of 128K.
## Evaluations
Command R+ has been submitted to the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We include the results below, along with a direct comparison to the strongest state-of-art open weights models currently available on Hugging Face. We note that these results are only useful to compare when evaluations are implemented for all models in a [standardized way](https://github.com/EleutherAI/lm-evaluation-harness) using publically available code, and hence shouldn't be used for comparison outside of models submitted to the leaderboard or compared to self-reported numbers which can't be replicated in the same way.
| Model | Average | Arc (Challenge) | Hella Swag | MMLU | Truthful QA | Winogrande | GSM8k |
|:--------------------------------|----------:|------------------:|-------------:|-------:|--------------:|-------------:|--------:|
| **CohereForAI/c4ai-command-r-plus** | 74.6 | 70.99 | 88.6 | 75.7 | 56.3 | 85.4 | 70.7 |
| [DBRX Instruct](https://huggingface.co/databricks/dbrx-instruct) | 74.5 | 68.9 | 89 | 73.7 | 66.9 | 81.8 | 66.9 |
| [Mixtral 8x7B-Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.7 | 70.1 | 87.6 | 71.4 | 65 | 81.1 | 61.1 |
| [Mixtral 8x7B Chat](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.6 | 70.2 | 87.6 | 71.2 | 64.6 | 81.4 | 60.7 |
| [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) | 68.5 | 65.5 | 87 | 68.2 | 52.3 | 81.5 | 56.6 |
| [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf) | 67.9 | 67.3 | 87.3 | 69.8 | 44.9 | 83.7 | 54.1 |
| [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 65.3 | 65.4 | 84.2 | 74.9 | 55.4 | 80.1 | 31.9 |
| [Gemma-7B](https://huggingface.co/google/gemma-7b) | 63.8 | 61.1 | 82.2 | 64.6 | 44.8 | 79 | 50.9 |
| [LLama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | 62.4 | 64.6 | 85.9 | 63.9 | 52.8 | 80.5 | 26.7 |
| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 61 | 60 | 83.3 | 64.2 | 42.2 | 78.4 | 37.8 |
We include these metrics here because they are frequently requested, but note that these metrics do not capture RAG, multilingual, tooling performance or the evaluation of open ended generations which we believe Command R+ to be state-of-art at. For evaluations of RAG, multilingual and tooling read more [here](https://txt.cohere.com/command-r-plus-microsoft-azure/). For evaluation of open ended generation, Command R+ is currently being evaluated on the [chatbot arena](https://chat.lmsys.org/).
### Tool use & multihop capabilities:
Command R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.
Command R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.
The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.
We recommend including the `directly_answer` tool, but it can be removed or renamed if required.
Comprehensive documentation for working with command R+'s tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
The code snippet below shows a minimal working example on how to render a prompt.
<details>
<summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary>
```python
from transformers import AutoTokenizer
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# define conversation input:
conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
# Define tools available for the model to use:
tools = [
{
"name": "internet_search",
"description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
"parameter_definitions": {
"query": {
"description": "Query to search the internet with",
"type": 'str',
"required": True
}
}
},
{
'name': "directly_answer",
"description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
'parameter_definitions': {}
}
]
# render the tool use prompt as a string:
tool_use_prompt = tokenizer.apply_tool_use_template(
conversation,
tools=tools,
tokenize=False,
add_generation_prompt=True,
)
print(tool_use_prompt)
```
</details>
<details>
<summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary>
````
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
# System Preamble
## Basic Rules
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
# User Preamble
## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.
## Available Tools
Here is a list of tools that you have available to you:
```python
def internet_search(query: str) -> List[Dict]:
"""Returns a list of relevant document snippets for a textual query retrieved from the internet
Args:
query (str): Query to search the internet with
"""
pass
```
```python
def directly_answer() -> List[Dict]:
"""Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
"""
pass
```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
```json
[
{
"tool_name": title of the tool in the specification,
"parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
}
]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>
<details>
<summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary>
````
Action: ```json
[
{
"tool_name": "internet_search",
"parameters": {
"query": "biggest penguin in the world"
}
}
]
```
````
</details>
### Grounded Generation and RAG Capabilities:
Command R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.
Command R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
By default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation.
The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
Comprehensive documentation for working with Command R+'s grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
The code snippet below shows a minimal working example on how to render a prompt.
<details>
<summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary>
````python
from transformers import AutoTokenizer
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# define conversation input:
conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
# define documents to ground on:
documents = [
{ "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." },
{ "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
]
# render the tool use prompt as a string:
grounded_generation_prompt = tokenizer.apply_grounded_generation_template(
conversation,
documents=documents,
citation_mode="accurate", # or "fast"
tokenize=False,
add_generation_prompt=True,
)
print(grounded_generation_prompt)
````
</details>
<details>
<summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary>
````<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
# System Preamble
## Basic Rules
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
# User Preamble
## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results>
Document: 0
title: Tall penguins
text: Emperor penguins are the tallest growing up to 122 cm in height.
Document: 1
title: Penguin habitats
text: Emperor penguins only live in Antarctica.
</results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line.
Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'.
Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'.
Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup.
Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>
<details>
<summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary>
````
Relevant Documents: 0,1
Cited Documents: 0,1
Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres.
Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0>
````
</details>
### Code Capabilities:
Command R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
### Model Card Contact
For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai).
### Terms of Use:
We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).
### Try Chat:
You can try Command R+ chat in the playground [here](https://dashboard.cohere.com/playground/chat). You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus).
|
billa1972/layoutlmv3-violations-test
|
billa1972
| 2024-06-29T01:26:59Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:violations",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-06-29T01:14:58Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- violations
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-violations-test
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: violations
type: violations
config: ViolationsExtraction
split: test
args: ViolationsExtraction
metrics:
- name: Precision
type: precision
value: 0.9482758620689655
- name: Recall
type: recall
value: 0.9116022099447514
- name: F1
type: f1
value: 0.9295774647887324
- name: Accuracy
type: accuracy
value: 0.9502762430939227
---
<!-- 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. -->
# layoutlmv3-violations-test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the violations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3685
- Precision: 0.9483
- Recall: 0.9116
- F1: 0.9296
- Accuracy: 0.9503
## 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
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 9.0909 | 100 | 0.2997 | 0.9543 | 0.9227 | 0.9382 | 0.9558 |
| No log | 18.1818 | 200 | 0.3729 | 0.9425 | 0.9061 | 0.9239 | 0.9448 |
| No log | 27.2727 | 300 | 0.3408 | 0.9543 | 0.9227 | 0.9382 | 0.9558 |
| No log | 36.3636 | 400 | 0.3566 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 45.4545 | 500 | 0.3685 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 54.5455 | 600 | 0.3736 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 63.6364 | 700 | 0.3866 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 72.7273 | 800 | 0.3990 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.0997 | 81.8182 | 900 | 0.4018 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
| 0.001 | 90.9091 | 1000 | 0.3979 | 0.9483 | 0.9116 | 0.9296 | 0.9503 |
### Framework versions
- Transformers 4.42.1
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|
mpasila/faster-whisper-large-finnish-v3
|
mpasila
| 2024-06-29T01:21:39Z | 14 | 2 |
transformers
|
[
"transformers",
"whisper-event",
"finnish",
"speech-recognition",
"automatic-speech-recognition",
"fi",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-29T00:43:24Z |
---
language:
- fi
license: apache-2.0
tags:
- whisper-event
- finnish
- speech-recognition
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
- cer
model-index:
- name: Whisper Large V3 Finnish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: fi
split: test
args: fi
metrics:
- name: Wer
type: wer
value: 8.23
- name: Cer
type: cer
value: 1.43
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: FLEURS
type: google/fleurs
config: fi_fi
split: test
args: fi_fi
metrics:
- name: Wer
type: wer
value: 8.21
- name: Cer
type: cer
value: 3.23
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
# This is a conversion of [Finnish-NLP/whisper-large-finnish-v3](https://huggingface.co/Finnish-NLP/whisper-large-finnish-v3) into faster-whisper format.
<h3>This is our improved Whisper v3 model that is now finetuned from OpenAI Whisper Large V3 </h3>
<p>We improve from our previously finetuned Whisper V2 model in the following manner<a>https://huggingface.co/Finnish-NLP/whisper-large-v2-finnish</a> </p>
<p>CV11 (Common Voice 11 test set) WER (Word error rate) 10.42 --> 8.23</p>
<p>Fleurs (A speech recognition test set by Google) WER (Word error rate) 10.20 --> 8.21</p>
<p>Model was trained on Nvidia RTX4080 for 32k steps with batch size 8, gradient accumulation 2</p>
<br>
<h3> Original OpenAI Whisper Large V3</h3>
- CV11
- WER: 14.81
- WER NORMALIZED: 10.82
- CER: 2.7
- CER NORMALIZED: 2.07
- Fleurs
- WER: 12.04
- WER NORMALIZED: 9.63
- CER: 2.48
- CER NORMALIZED: 3.64
<h3> After Finetuning with Finnish data our V3 got these scores on the test set:</h3>
- @14000 finetuning steps
- CV11
- WER: 11.36
- WER NORMALIZED: 8.31
- CER: 1.93
- CER NORMALIZED: 1.48
- Fleurs
- WER: 10.2
- WER NORMALIZED: 8.56
- CER: 2.26
- CER NORMALIZED: 3.54
- @32000 finetuning steps
- CV11
- WER: 11.47
- WER NORMALIZED: 8.23
- CER: 1.91
- CER NORMALIZED: 1.43
- Fleurs
- WER: 10.1
- WER NORMALIZED: 8.21
- CER: 2.2
- CER NORMALIZED: 3.23
|
Xu-Ouyang/pythia-12b-deduped-int4-step107000-GPTQ-wikitext2
|
Xu-Ouyang
| 2024-06-29T01:05:54Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-06-29T01:02:53Z |
---
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]
- **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]
|
Fathima-Firose/Inspectra-Sum
|
Fathima-Firose
| 2024-06-29T01:02:07Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-06-29T01:01:44Z |
---
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]
|
grocode87/replyability_model
|
grocode87
| 2024-06-29T00:56:28Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-29T00:56:23Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# grocode87/replyability_model
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('grocode87/replyability_model')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=grocode87/replyability_model)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 119 with parameters:
```
{'batch_size': 200, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Sharan1712/llama2_7B_oasst_qlora_4bit_1e
|
Sharan1712
| 2024-06-29T00:51:27Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-29T00:49:05Z |
---
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]
|
John6666/mala-anime-mix-nsfw-pony-xl-v5-sdxl
|
John6666
| 2024-06-29T00:47:45Z | 19,258 | 13 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-06-29T00:41:09Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- pony
---
Original model is [here](https://civitai.com/models/442163/mala-anime-mix-nsfw-ponyxl?modelVersionId=604755).
|
John6666/3x3x3mixxl-v2-sdxl
|
John6666
| 2024-06-29T00:35:13Z | 14,515 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"pony",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-06-29T00:22:29Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- pony
---
Original model is [here](https://civitai.com/models/464044?modelVersionId=605542).
|
opencsg/csg-wukong-code-1B-cpt
|
opencsg
| 2024-06-29T00:00:33Z | 124 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T17:54:24Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- code
license: apache-2.0
---
# **csg-wukong-code-1B-cpt** [[中文]](#chinese) [[English]](#english)
<a id="english"></a>
<p align="center">
<img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg">
</p>
<p align="center"><a href="https://portal.opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p>
</div>
OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models.
The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively.
## Model Description
**csg-wukong-1B-code-cpt** is a 1 billion-parameter small language model(SLM) continue pretrained based on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B).
<br>
we will introduce more information about csg-wukong-code-1B-cpt.
# Training
## Hardware
- **GPUs:** 16 H800
- **Training time:** 5days
## Software
- **Orchestration:** [Deepspeed](https://github.com/OpenCSGs)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
<a id="chinese"></a>
<p>
</p>
# OpenCSG介绍
<p align="center">
<img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg">
</p>
<p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p>
</div>
OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。
OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。 我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。
## 模型介绍
**csg-wukong-1B-code-cpt** 是一个1B参数量的小语言模型，该模型是在[csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B),二次预训练二成
<br>
我们将在后面介绍更多关于这个模型的信息。
# 训练
## 硬件资源
- **GPU数量：** 16 H800
- **训练时间：** 5天
## 软件使用
- **微调训练框架：** [Deepspeed](https://github.com/OpenCSGs)
- **深度学习框架：** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16：** [apex](https://github.com/NVIDIA/apex)
|
RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf
|
RichardErkhov
| 2024-06-28T23:54:11Z | 11 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-28T18:52:15Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
T3Q-LLM1-Solar-10.8B-v1.0 - GGUF
- Model creator: https://huggingface.co/T3Q-LLM-Product/
- Original model: https://huggingface.co/T3Q-LLM-Product/T3Q-LLM1-Solar-10.8B-v1.0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q2_K.gguf) | Q2_K | 3.77GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ3_XS.gguf) | IQ3_XS | 4.18GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ3_S.gguf) | IQ3_S | 4.41GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_S.gguf) | Q3_K_S | 4.39GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ3_M.gguf) | IQ3_M | 4.56GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K.gguf) | Q3_K | 4.88GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_M.gguf) | Q3_K_M | 4.88GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_L.gguf) | Q3_K_L | 5.31GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ4_XS.gguf) | IQ4_XS | 5.47GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_0.gguf) | Q4_0 | 5.7GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ4_NL.gguf) | IQ4_NL | 5.77GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_S.gguf) | Q4_K_S | 5.75GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_K.gguf) | Q4_K | 6.07GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_M.gguf) | Q4_K_M | 6.07GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_1.gguf) | Q4_1 | 6.32GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_0.gguf) | Q5_0 | 6.94GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_S.gguf) | Q5_K_S | 6.94GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_K.gguf) | Q5_K | 7.13GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_M.gguf) | Q5_K_M | 7.13GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_1.gguf) | Q5_1 | 7.56GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q6_K.gguf) | Q6_K | 8.26GB |
| [T3Q-LLM1-Solar-10.8B-v1.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q8_0.gguf) | Q8_0 | 10.69GB |
Original model description:
---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
|
tgrhn/whisper-large-v2-tr-cv17-2
|
tgrhn
| 2024-06-28T23:36:57Z | 84 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"tr",
"dataset:mozilla-foundation/common_voice_17",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-28T19:58:08Z |
---
language:
- tr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17
model-index:
- name: 'Whisper Large v2 TR '
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. -->
# Whisper Large v2 TR
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1520
## 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: 128
- eval_batch_size: 128
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 363 | 0.1495 |
| 0.3301 | 2.0 | 726 | 0.1448 |
| 0.0633 | 3.0 | 1089 | 0.1520 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Xu-Ouyang/pythia-12b-deduped-int4-step71000-GPTQ-wikitext2
|
Xu-Ouyang
| 2024-06-28T23:33:58Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-06-28T23:30:59Z |
---
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]
|
bhavsh/tinyllama-new-bhavesh
|
bhavsh
| 2024-06-28T23:19:25Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T23:17:02Z |
---
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]
- **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]
|
ZeroWw/gemma-2-9b-it-GGUF
|
ZeroWw
| 2024-06-28T23:16:08Z | 39 | 1 | null |
[
"gguf",
"en",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-28T22:57:42Z |
---
license: mit
language:
- en
---
My own (ZeroWw) quantizations.
output and embed tensors quantized to f16.
all other tensors quantized to q5_k or q6_k.
Result:
both f16.q6 and f16.q5 are smaller than q8_0 standard quantization
and they perform as well as the pure f16.
|
liminerity/Bitnet-Mistral.0.2-v6.9
|
liminerity
| 2024-06-28T23:04:10Z | 156 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:liminerity/Bitnet-Mistral.0.2-v6.9",
"base_model:finetune:liminerity/Bitnet-Mistral.0.2-v6.9",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T01:17:15Z |
---
base_model: liminerity/Bitnet-Mistral.0.2-v6.9
tags:
- generated_from_trainer
model-index:
- name: Bitnet-Mistral.0.2-v6.9
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. -->
# Bitnet-Mistral.0.2-v6.9
This model is a fine-tuned version of [liminerity/Bitnet-Mistral.0.2-v6.9](https://huggingface.co/liminerity/Bitnet-Mistral.0.2-v6.9) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
DewEfresh/neo_7b-slerp
|
DewEfresh
| 2024-06-28T23:03:27Z | 82 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"m-a-p/neo_7b",
"conversational",
"base_model:m-a-p/neo_7b",
"base_model:finetune:m-a-p/neo_7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T23:00:23Z |
---
base_model:
- m-a-p/neo_7b
- m-a-p/neo_7b
tags:
- merge
- mergekit
- lazymergekit
- m-a-p/neo_7b
---
# neo_7b-slerp
neo_7b-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [m-a-p/neo_7b](https://huggingface.co/m-a-p/neo_7b)
* [m-a-p/neo_7b](https://huggingface.co/m-a-p/neo_7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: m-a-p/neo_7b
layer_range: [0, 1]
- model: m-a-p/neo_7b
layer_range: [1, 2]
- sources:
- model: m-a-p/neo_7b
layer_range: [2, 3]
- model: m-a-p/neo_7b
layer_range: [3, 4]
- sources:
- model: m-a-p/neo_7b
layer_range: [4, 5]
- model: m-a-p/neo_7b
layer_range: [5,6]
- sources:
- model: m-a-p/neo_7b
layer_range: [6, 7]
- model: m-a-p/neo_7b
layer_range: [7, 8]
- sources:
- model: m-a-p/neo_7b
layer_range: [8, 9]
- model: m-a-p/neo_7b
layer_range: [9, 10]
- sources:
- model: m-a-p/neo_7b
layer_range: [10, 11]
- model: m-a-p/neo_7b
layer_range: [11, 12]
- sources:
- model: m-a-p/neo_7b
layer_range: [12, 13]
- model: m-a-p/neo_7b
layer_range: [13, 14]
- sources:
- model: m-a-p/neo_7b
layer_range: [14, 15]
- model: m-a-p/neo_7b
layer_range: [15, 16]
- sources:
- model: m-a-p/neo_7b
layer_range: [16, 17]
- model: m-a-p/neo_7b
layer_range: [17, 18]
- sources:
- model: m-a-p/neo_7b
layer_range: [18, 19]
- model: m-a-p/neo_7b
layer_range: [19, 20]
- sources:
- model: m-a-p/neo_7b
layer_range: [20, 21]
- model: m-a-p/neo_7b
layer_range: [21, 22]
- sources:
- model: m-a-p/neo_7b
layer_range: [22, 23]
- model: m-a-p/neo_7b
layer_range: [23, 24]
- sources:
- model: m-a-p/neo_7b
layer_range: [24, 25]
- model: m-a-p/neo_7b
layer_range: [25, 26]
- sources:
- model: m-a-p/neo_7b
layer_range: [26, 27]
- model: m-a-p/neo_7b
layer_range: [27, 28]
merge_method: slerp
base_model: m-a-p/neo_7b
parameters:
t: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "DewEfresh/neo_7b-slerp"
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"])
```
|
jlancaster36/code_bagel_llama-3-8b-v1.1
|
jlancaster36
| 2024-06-28T22:38:45Z | 8,410 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:mattshumer/Llama-3-8B-16K",
"base_model:finetune:mattshumer/Llama-3-8B-16K",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T22:34:30Z |
---
base_model: mattshumer/Llama-3-8B-16K
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** jlancaster36
- **License:** apache-2.0
- **Finetuned from model :** mattshumer/Llama-3-8B-16K
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)
|
BigHuggyD/cohereforai_c4ai-command-r-plus_exl2_6.0bpw_h8
|
BigHuggyD
| 2024-06-28T22:19:27Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"cohere",
"text-generation",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-28T21:41:25Z |
---
inference: false
license: cc-by-nc-4.0
library_name: transformers
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
---
# Model Card for C4AI Command R+
🚨 **This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit)**.
## Model Summary
C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
C4AI Command R+ is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is [C4AI Command R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
Developed by: [Cohere](https://cohere.com/) and [Cohere For AI](https://cohere.for.ai)
- Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
- License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
- Model: c4ai-command-r-plus
- Model Size: 104 billion parameters
- Context length: 128K
**Try C4AI Command R+**
You can try out C4AI Command R+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus).
**Usage**
Please install `transformers` from the source repository that includes the necessary changes for this model.
```python
# pip install 'git+https://github.com/huggingface/transformers.git'
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
**Quantized model through bitsandbytes, 8-bit precision**
```python
# pip install 'git+https://github.com/huggingface/transformers.git' bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
**Quantized model through bitsandbytes, 4-bit precision**
This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit).
## Model Details
**Input**: Models input text only.
**Output**: Models generate text only.
**Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety.
**Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic.
Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.
**Context length**: Command R+ supports a context length of 128K.
## Evaluations
Command R+ has been submitted to the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We include the results below, along with a direct comparison to the strongest state-of-art open weights models currently available on Hugging Face. We note that these results are only useful to compare when evaluations are implemented for all models in a [standardized way](https://github.com/EleutherAI/lm-evaluation-harness) using publically available code, and hence shouldn't be used for comparison outside of models submitted to the leaderboard or compared to self-reported numbers which can't be replicated in the same way.
| Model | Average | Arc (Challenge) | Hella Swag | MMLU | Truthful QA | Winogrande | GSM8k |
|:--------------------------------|----------:|------------------:|-------------:|-------:|--------------:|-------------:|--------:|
| **CohereForAI/c4ai-command-r-plus** | 74.6 | 70.99 | 88.6 | 75.7 | 56.3 | 85.4 | 70.7 |
| [DBRX Instruct](https://huggingface.co/databricks/dbrx-instruct) | 74.5 | 68.9 | 89 | 73.7 | 66.9 | 81.8 | 66.9 |
| [Mixtral 8x7B-Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.7 | 70.1 | 87.6 | 71.4 | 65 | 81.1 | 61.1 |
| [Mixtral 8x7B Chat](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.6 | 70.2 | 87.6 | 71.2 | 64.6 | 81.4 | 60.7 |
| [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) | 68.5 | 65.5 | 87 | 68.2 | 52.3 | 81.5 | 56.6 |
| [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf) | 67.9 | 67.3 | 87.3 | 69.8 | 44.9 | 83.7 | 54.1 |
| [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 65.3 | 65.4 | 84.2 | 74.9 | 55.4 | 80.1 | 31.9 |
| [Gemma-7B](https://huggingface.co/google/gemma-7b) | 63.8 | 61.1 | 82.2 | 64.6 | 44.8 | 79 | 50.9 |
| [LLama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | 62.4 | 64.6 | 85.9 | 63.9 | 52.8 | 80.5 | 26.7 |
| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 61 | 60 | 83.3 | 64.2 | 42.2 | 78.4 | 37.8 |
We include these metrics here because they are frequently requested, but note that these metrics do not capture RAG, multilingual, tooling performance or the evaluation of open ended generations which we believe Command R+ to be state-of-art at. For evaluations of RAG, multilingual and tooling read more [here](https://txt.cohere.com/command-r-plus-microsoft-azure/). For evaluation of open ended generation, Command R+ is currently being evaluated on the [chatbot arena](https://chat.lmsys.org/).
### Tool use & multihop capabilities:
Command R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.
Command R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.
The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.
We recommend including the `directly_answer` tool, but it can be removed or renamed if required.
Comprehensive documentation for working with command R+'s tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
The code snippet below shows a minimal working example on how to render a prompt.
<details>
<summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary>
```python
from transformers import AutoTokenizer
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# define conversation input:
conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
# Define tools available for the model to use:
tools = [
{
"name": "internet_search",
"description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
"parameter_definitions": {
"query": {
"description": "Query to search the internet with",
"type": 'str',
"required": True
}
}
},
{
'name': "directly_answer",
"description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
'parameter_definitions': {}
}
]
# render the tool use prompt as a string:
tool_use_prompt = tokenizer.apply_tool_use_template(
conversation,
tools=tools,
tokenize=False,
add_generation_prompt=True,
)
print(tool_use_prompt)
```
</details>
<details>
<summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary>
````
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
# System Preamble
## Basic Rules
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
# User Preamble
## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.
## Available Tools
Here is a list of tools that you have available to you:
```python
def internet_search(query: str) -> List[Dict]:
"""Returns a list of relevant document snippets for a textual query retrieved from the internet
Args:
query (str): Query to search the internet with
"""
pass
```
```python
def directly_answer() -> List[Dict]:
"""Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
"""
pass
```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
```json
[
{
"tool_name": title of the tool in the specification,
"parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
}
]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>
<details>
<summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary>
````
Action: ```json
[
{
"tool_name": "internet_search",
"parameters": {
"query": "biggest penguin in the world"
}
}
]
```
````
</details>
### Grounded Generation and RAG Capabilities:
Command R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.
Command R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
By default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation.
The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
Comprehensive documentation for working with Command R+'s grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
The code snippet below shows a minimal working example on how to render a prompt.
<details>
<summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary>
````python
from transformers import AutoTokenizer
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# define conversation input:
conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
# define documents to ground on:
documents = [
{ "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." },
{ "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
]
# render the tool use prompt as a string:
grounded_generation_prompt = tokenizer.apply_grounded_generation_template(
conversation,
documents=documents,
citation_mode="accurate", # or "fast"
tokenize=False,
add_generation_prompt=True,
)
print(grounded_generation_prompt)
````
</details>
<details>
<summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary>
````<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
# System Preamble
## Basic Rules
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
# User Preamble
## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results>
Document: 0
title: Tall penguins
text: Emperor penguins are the tallest growing up to 122 cm in height.
Document: 1
title: Penguin habitats
text: Emperor penguins only live in Antarctica.
</results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line.
Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'.
Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'.
Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup.
Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>
<details>
<summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary>
````
Relevant Documents: 0,1
Cited Documents: 0,1
Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres.
Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0>
````
</details>
### Code Capabilities:
Command R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
### Model Card Contact
For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai).
### Terms of Use:
We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).
### Try Chat:
You can try Command R+ chat in the playground [here](https://dashboard.cohere.com/playground/chat). You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus).
|
jeiku/qwen2-1
|
jeiku
| 2024-06-28T22:18:55Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Weyaxi/Einstein-v7-Qwen2-7B",
"jeiku/dontusethis",
"conversational",
"base_model:Weyaxi/Einstein-v7-Qwen2-7B",
"base_model:merge:Weyaxi/Einstein-v7-Qwen2-7B",
"base_model:jeiku/dontusethis",
"base_model:merge:jeiku/dontusethis",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T22:14:06Z |
---
base_model:
- Weyaxi/Einstein-v7-Qwen2-7B
- jeiku/dontusethis
tags:
- merge
- mergekit
- lazymergekit
- Weyaxi/Einstein-v7-Qwen2-7B
- jeiku/dontusethis
---
# qwen2-1
qwen2-1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Weyaxi/Einstein-v7-Qwen2-7B](https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B)
* [jeiku/dontusethis](https://huggingface.co/jeiku/dontusethis)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Weyaxi/Einstein-v7-Qwen2-7B
layer_range: [0,28]
- model: jeiku/dontusethis
layer_range: [0,28]
merge_method: slerp
base_model: Weyaxi/Einstein-v7-Qwen2-7B
parameters:
t:
- filter: self_attn
value: [0, 0.3, 0.5, 0.7, 1]
- filter: mlp
value: [1, 0.7, 0.5, 0.3, 0]
- value: 0.33
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jeiku/qwen2-1"
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"])
```
|
martimfasantos/tinyllama-1.1b-sum-dpo-full_LR5e-8_BS64_3epochs_old
|
martimfasantos
| 2024-06-28T22:06:02Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:openai/summarize_from_feedback",
"base_model:martimfasantos/tinyllama-1.1b-sum-sft-full_old",
"base_model:finetune:martimfasantos/tinyllama-1.1b-sum-sft-full_old",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-27T22:43:39Z |
---
license: apache-2.0
base_model: martimfasantos/tinyllama-1.1b-sum-sft-full_old
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- openai/summarize_from_feedback
model-index:
- name: tinyllama-1.1b-sum-dpo-full_LR5e-8_BS64_3epochs_old
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. -->
# tinyllama-1.1b-sum-dpo-full_LR5e-8_BS64_3epochs_old
This model is a fine-tuned version of [martimfasantos/tinyllama-1.1b-sum-sft-full_old](https://huggingface.co/martimfasantos/tinyllama-1.1b-sum-sft-full_old) on the openai/summarize_from_feedback dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6851
- Rewards/chosen: -0.0660
- Rewards/rejected: -0.0839
- Rewards/accuracies: 0.5978
- Rewards/margins: 0.0179
- Logps/rejected: -71.5685
- Logps/chosen: -65.3140
- Logits/rejected: -3.0328
- Logits/chosen: -3.0386
## 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-08
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6931 | 0.0689 | 100 | 0.6932 | -0.0000 | 0.0001 | 0.4809 | -0.0001 | -63.1742 | -58.7157 | -3.1575 | -3.1631 |
| 0.6931 | 0.1378 | 200 | 0.6932 | -0.0001 | -0.0000 | 0.4735 | -0.0001 | -63.1804 | -58.7190 | -3.1577 | -3.1633 |
| 0.693 | 0.2068 | 300 | 0.6931 | 0.0002 | 0.0002 | 0.5044 | 0.0000 | -63.1651 | -58.6934 | -3.1573 | -3.1630 |
| 0.6929 | 0.2757 | 400 | 0.6931 | 0.0004 | 0.0004 | 0.4928 | 0.0000 | -63.1405 | -58.6678 | -3.1565 | -3.1621 |
| 0.6925 | 0.3446 | 500 | 0.6930 | 0.0009 | 0.0005 | 0.5374 | 0.0004 | -63.1296 | -58.6253 | -3.1548 | -3.1605 |
| 0.6919 | 0.4135 | 600 | 0.6928 | 0.0012 | 0.0006 | 0.5644 | 0.0006 | -63.1213 | -58.5903 | -3.1529 | -3.1585 |
| 0.6917 | 0.4824 | 700 | 0.6926 | 0.0017 | 0.0006 | 0.5562 | 0.0011 | -63.1193 | -58.5436 | -3.1505 | -3.1562 |
| 0.6905 | 0.5513 | 800 | 0.6924 | 0.0019 | 0.0003 | 0.5681 | 0.0016 | -63.1495 | -58.5180 | -3.1471 | -3.1528 |
| 0.6898 | 0.6203 | 900 | 0.6920 | 0.0018 | -0.0004 | 0.5839 | 0.0023 | -63.2244 | -58.5291 | -3.1427 | -3.1484 |
| 0.6894 | 0.6892 | 1000 | 0.6918 | 0.0013 | -0.0015 | 0.5699 | 0.0028 | -63.3282 | -58.5803 | -3.1380 | -3.1437 |
| 0.6894 | 0.7581 | 1100 | 0.6915 | 0.0004 | -0.0030 | 0.5718 | 0.0033 | -63.4761 | -58.6734 | -3.1327 | -3.1383 |
| 0.6886 | 0.8270 | 1200 | 0.6912 | -0.0007 | -0.0048 | 0.5704 | 0.0041 | -63.6618 | -58.7859 | -3.1285 | -3.1342 |
| 0.6878 | 0.8959 | 1300 | 0.6907 | -0.0026 | -0.0077 | 0.5802 | 0.0051 | -63.9501 | -58.9768 | -3.1220 | -3.1276 |
| 0.6872 | 0.9649 | 1400 | 0.6904 | -0.0047 | -0.0104 | 0.5869 | 0.0057 | -64.2244 | -59.1855 | -3.1181 | -3.1238 |
| 0.6865 | 1.0338 | 1500 | 0.6902 | -0.0077 | -0.0140 | 0.5869 | 0.0063 | -64.5792 | -59.4787 | -3.1117 | -3.1174 |
| 0.6855 | 1.1027 | 1600 | 0.6898 | -0.0109 | -0.0180 | 0.5839 | 0.0071 | -64.9847 | -59.8052 | -3.1071 | -3.1128 |
| 0.6842 | 1.1716 | 1700 | 0.6895 | -0.0156 | -0.0234 | 0.5827 | 0.0079 | -65.5234 | -60.2681 | -3.1002 | -3.1059 |
| 0.6842 | 1.2405 | 1800 | 0.6890 | -0.0215 | -0.0304 | 0.5876 | 0.0089 | -66.2193 | -60.8594 | -3.0947 | -3.1005 |
| 0.6804 | 1.3094 | 1900 | 0.6888 | -0.0253 | -0.0347 | 0.5911 | 0.0095 | -66.6540 | -61.2379 | -3.0896 | -3.0952 |
| 0.6827 | 1.3784 | 2000 | 0.6883 | -0.0299 | -0.0405 | 0.5971 | 0.0107 | -67.2341 | -61.6997 | -3.0847 | -3.0904 |
| 0.6805 | 1.4473 | 2100 | 0.6879 | -0.0345 | -0.0461 | 0.5980 | 0.0116 | -67.7896 | -62.1622 | -3.0798 | -3.0855 |
| 0.68 | 1.5162 | 2200 | 0.6876 | -0.0374 | -0.0495 | 0.5929 | 0.0121 | -68.1323 | -62.4511 | -3.0751 | -3.0808 |
| 0.6805 | 1.5851 | 2300 | 0.6873 | -0.0420 | -0.0550 | 0.5908 | 0.0130 | -68.6762 | -62.9119 | -3.0705 | -3.0763 |
| 0.6802 | 1.6540 | 2400 | 0.6870 | -0.0440 | -0.0575 | 0.5936 | 0.0135 | -68.9288 | -63.1075 | -3.0657 | -3.0714 |
| 0.6788 | 1.7229 | 2500 | 0.6868 | -0.0465 | -0.0604 | 0.5950 | 0.0140 | -69.2231 | -63.3570 | -3.0616 | -3.0674 |
| 0.6784 | 1.7919 | 2600 | 0.6865 | -0.0493 | -0.0639 | 0.5948 | 0.0146 | -69.5742 | -63.6419 | -3.0568 | -3.0626 |
| 0.6771 | 1.8608 | 2700 | 0.6863 | -0.0524 | -0.0676 | 0.5943 | 0.0152 | -69.9422 | -63.9527 | -3.0530 | -3.0588 |
| 0.676 | 1.9297 | 2800 | 0.6861 | -0.0553 | -0.0710 | 0.5892 | 0.0157 | -70.2780 | -64.2370 | -3.0501 | -3.0558 |
| 0.6793 | 1.9986 | 2900 | 0.6860 | -0.0571 | -0.0731 | 0.5922 | 0.0160 | -70.4908 | -64.4251 | -3.0474 | -3.0532 |
| 0.6755 | 2.0675 | 3000 | 0.6858 | -0.0592 | -0.0755 | 0.5929 | 0.0163 | -70.7265 | -64.6294 | -3.0442 | -3.0500 |
| 0.678 | 2.1365 | 3100 | 0.6856 | -0.0600 | -0.0768 | 0.5941 | 0.0168 | -70.8605 | -64.7164 | -3.0422 | -3.0480 |
| 0.6795 | 2.2054 | 3200 | 0.6855 | -0.0611 | -0.0781 | 0.5941 | 0.0170 | -70.9855 | -64.8209 | -3.0400 | -3.0457 |
| 0.6784 | 2.2743 | 3300 | 0.6854 | -0.0619 | -0.0791 | 0.5969 | 0.0172 | -71.0930 | -64.9018 | -3.0382 | -3.0440 |
| 0.6792 | 2.3432 | 3400 | 0.6853 | -0.0627 | -0.0801 | 0.5946 | 0.0175 | -71.1919 | -64.9777 | -3.0366 | -3.0423 |
| 0.6769 | 2.4121 | 3500 | 0.6853 | -0.0636 | -0.0811 | 0.5953 | 0.0175 | -71.2883 | -65.0695 | -3.0356 | -3.0414 |
| 0.6771 | 2.4810 | 3600 | 0.6852 | -0.0645 | -0.0822 | 0.5978 | 0.0177 | -71.3953 | -65.1583 | -3.0346 | -3.0404 |
| 0.6785 | 2.5500 | 3700 | 0.6851 | -0.0650 | -0.0829 | 0.5997 | 0.0179 | -71.4696 | -65.2152 | -3.0340 | -3.0397 |
| 0.6779 | 2.6189 | 3800 | 0.6851 | -0.0655 | -0.0833 | 0.5962 | 0.0179 | -71.5138 | -65.2594 | -3.0332 | -3.0390 |
| 0.6775 | 2.6878 | 3900 | 0.6851 | -0.0657 | -0.0836 | 0.5974 | 0.0179 | -71.5451 | -65.2842 | -3.0331 | -3.0389 |
| 0.6757 | 2.7567 | 4000 | 0.6851 | -0.0658 | -0.0837 | 0.5985 | 0.0179 | -71.5477 | -65.2925 | -3.0326 | -3.0384 |
| 0.6759 | 2.8256 | 4100 | 0.6850 | -0.0658 | -0.0839 | 0.6022 | 0.0181 | -71.5705 | -65.2951 | -3.0324 | -3.0382 |
| 0.6755 | 2.8946 | 4200 | 0.6852 | -0.0659 | -0.0838 | 0.5990 | 0.0178 | -71.5600 | -65.3068 | -3.0326 | -3.0384 |
| 0.6803 | 2.9635 | 4300 | 0.6852 | -0.0659 | -0.0838 | 0.6006 | 0.0179 | -71.5612 | -65.3069 | -3.0327 | -3.0385 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|
dadashzadeh/mbart-finetuned-fa-pretrained-mmad
|
dadashzadeh
| 2024-06-28T21:58:28Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"summarization",
"fa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-22T18:19:32Z |
---
tags:
- generated_from_trainer
model-index:
- name: mbart-finetuned-fa-pretrained-mmad
results: []
pipeline_tag: summarization
license: mit
language:
- fa
---
<!-- 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. -->
# mbart-finetuned-fa-pretrained-mmad
This model is a fine-tuned version of [eslamxm/mbart-finetuned-fa](https://huggingface.co/eslamxm/mbart-finetuned-fa) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0
- 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: 2
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BenevolenceMessiah/Llama-3-Instruct-8B-SPPO-Iter3-GGUF
|
BenevolenceMessiah
| 2024-06-28T21:57:37Z | 13 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:openbmb/UltraFeedback",
"base_model:UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3",
"base_model:quantized:UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2024-06-27T23:32:43Z |
---
base_model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
datasets:
- openbmb/UltraFeedback
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
Greetings friends, Asalamu Alaikum; I am pleased to provide you with GGUF vresions of this great model! The original model is: [Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) by [UCLA-AGI/](https://huggingface.co/UCLA-AGI)
---
<!-- description start -->
## Description (per [TheBloke](https://huggingface.co/TheBloke))
This repo contains GGUF format model files.
These files were quantised using ggml-org/gguf-my-repo [https://huggingface.co/spaces/ggml-org/gguf-my-repo]
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF (per [TheBloke](https://huggingface.co/TheBloke))
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
---
---
# BenevolenceMessiah/Llama-3-Instruct-8B-SPPO-Iter3-Q8_0-GGUF
This model was converted to GGUF format from [`UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3`](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo BenevolenceMessiah/Llama-3-Instruct-8B-SPPO-Iter3-Q8_0-GGUF --hf-file llama-3-instruct-8b-sppo-iter3-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BenevolenceMessiah/Llama-3-Instruct-8B-SPPO-Iter3-Q8_0-GGUF --hf-file llama-3-instruct-8b-sppo-iter3-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BenevolenceMessiah/Llama-3-Instruct-8B-SPPO-Iter3-Q8_0-GGUF --hf-file llama-3-instruct-8b-sppo-iter3-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BenevolenceMessiah/Llama-3-Instruct-8B-SPPO-Iter3-Q8_0-GGUF --hf-file llama-3-instruct-8b-sppo-iter3-q8_0.gguf -c 2048
```
|
ekaterina-blatova-jb/model_lr1e-4_old_scheduler_with_t_max_275_all_v4
|
ekaterina-blatova-jb
| 2024-06-28T21:57:24Z | 170 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T21:56:00Z |
---
{}
---
## Evaluation results
Validation loss on the whole input: 0.8525515561923385
Validation loss on completion: 0.9319955081446096
|
juan071/my-super-model
|
juan071
| 2024-06-28T21:43:39Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-28T21:34:37Z |
---
base_model: bert-base-cased
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my-super-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-super-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6064
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5353 | 0.5 | 5 | 1.6092 |
| 1.6015 | 1.0 | 10 | 1.6064 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
tricodex/Robobo-Learning-Machines
|
tricodex
| 2024-06-28T21:40:23Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2024-06-07T18:55:30Z |
---
license: gpl-3.0
---
Framework: https://github.com/ci-group/learning_machines_robobo/tree/master
Sim: https://www.coppeliarobotics.com/
Task 0:
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task0_g6.py
Task 1:
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task1_ppo_train.py
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task1_robobo_con_env.py
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task1_ppo_eval.py
Task 2:
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task2_ppo_train.py
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task2_robobo_v1_env.py
Task 2 Rework:
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task2_robobo_env_t3rework.py
Task 3:
https://huggingface.co/tricodex/Robobo-Learning-Machines/blob/main/learning_machines_robobo/examples/full_project_setup/catkin_ws/src/learning_machines/src/learning_machines/task3_rob_env_irs.py
|
grammarly/medit-xl
|
grammarly
| 2024-06-28T21:39:52Z | 0 | 5 |
transformers
|
[
"transformers",
"text2text-generation",
"en",
"de",
"es",
"ar",
"ja",
"ko",
"zh",
"dataset:wi_locness",
"dataset:matejklemen/falko_merlin",
"dataset:paws",
"dataset:paws-x",
"dataset:asset",
"arxiv:2402.16472",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-15T21:26:24Z |
---
license: cc-by-nc-sa-4.0
datasets:
- wi_locness
- matejklemen/falko_merlin
- paws
- paws-x
- asset
language:
- en
- de
- es
- ar
- ja
- ko
- zh
metrics:
- bleu
- rouge
- sari
- accuracy
library_name: transformers
widget:
- text: >-
Umschreiben sie den satz: When I grow up, I start to understand what he said
is quite right.
example_title: GEC (de|en)
- text: >-
문장의 간단한 버전 작성: Cuando se pueden mantener tasas de flujo comparables, los
resultados son altos.
example_title: Simplification (ko|es)
- text: 'Paraphrase this: いちごは物語を紹介し、読者をイベントに導くと彼は言った。'
example_title: Paraphrase (en|ja)
pipeline_tag: text2text-generation
---
# Model Card for mEdIT-xl
The `medit-xl` model was obtained by fine-tuning the `MBZUAI/bactrian-x-llama-7b-lora` model on the mEdIT dataset.
**Paper:** mEdIT: Multilingual Text Editing via Instruction Tuning
**Authors:** Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar
## Model Details
### Model Description
- **Language(s) (NLP)**: Arabic, Chinese, English, German, Japanese, Korean, Spanish
- **Finetuned from model:** `MBZUAI/bactrian-x-llama-7b-lora`
### Model Sources
- **Repository:** https://github.com/vipulraheja/medit
- **Paper:** https://arxiv.org/abs/2402.16472v1
## How to use
Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
Specifically, our models support both multi-lingual and cross-lingual text revision. Note that the input and output texts are always in the same language. The monolingual
vs. cross-lingual setting is determined by comparing the language of the edit instruction in relation to the language of the input text.
### Instruction format
Adherence to the following instruction format is essential; failure to do so may result in the model producing less-than-ideal results.
```
instruction_tokens = [
"Instruction",
"Anweisung",
...
]
input_tokens = [
"Input",
"Aporte",
...
]
output_tokens = [
"Output",
"Produzione",
...
]
task_descriptions = [
"Fix grammatical errors in this sentence", # <-- GEC task
"Umschreiben Sie den Satz", # <-- Paraphrasing
...
]
```
**The entire list of possible instructions, input/output tokens, and task descriptions can be found in the Appendix of our paper.**
```
prompt_template = """### <instruction_token>:\n<task_description>\n### <input_token>:\n<input>\n### <output_token>:\n\n"""
```
Note that the tokens and the task description need not be in the language of the input (in the case of cross-lingual revision).
### Run the model
**Make sure you have the following libraries installed:**
```
- peft
- protobuf
- sentencepiece
- tokenizers
- torch
- transformers
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "grammarly/medit-xl"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# English GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nI has small cat ,\n### 出力:\n\n'
inputs = tokenizer(prompt, return_tensors='pt')
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# --> I have a small cat ,
# German GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nIch haben eines kleines Katze ,\n### 出力:\n\n'
# ...
# --> Ich habe eine kleine Katze ,
```
#### Software
https://github.com/vipulraheja/medit
## Citation
**BibTeX:**
```
@article{raheja2023medit,
title={mEdIT: mEdIT: Multilingual Text Editing via Instruction Tuning},
author={Vipul Raheja and Dimitris Alikaniotis and Vivek Kulkarni and Bashar Alhafni and Dhruv Kumar},
year={2024},
eprint={2402.16472v1},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**APA:**
Raheja, V., Alikaniotis, D., Kulkarni, V., Alhafni, B., & Kumar, D. (2024). MEdIT: Multilingual Text Editing via Instruction Tuning. ArXiv. /abs/2402.16472
|
alexis779/Mistral-qlora-multilex
|
alexis779
| 2024-06-28T21:39:32Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:adapter:mistralai/Mistral-7B-v0.3",
"region:us"
] | null | 2024-06-19T06:29:43Z |
---
base_model: mistralai/Mistral-7B-v0.3
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
### Framework versions
- PEFT 0.11.1
|
ekaterina-blatova-jb/model_lr1e-4_old_scheduler_with_t_max_275_all_v2
|
ekaterina-blatova-jb
| 2024-06-28T21:10:56Z | 170 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T21:08:55Z |
---
{}
---
## Evaluation results
Validation loss on the whole input: 0.8578255325555801
Validation loss on completion: 0.9436061959131621
|
Edgar404/donut_tax
|
Edgar404
| 2024-06-28T21:07:48Z | 33 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-06-28T18:35:42Z |
---
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]
|
ILKT/2024-06-22_12-37-29_epoch_14
|
ILKT
| 2024-06-28T21:05:21Z | 148 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-23T08:48:59Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-22_12-37-29_epoch_14
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.04771371769384
- type: f1
value: 20.724204994614485
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 51.49999999999999
- type: ap
value: 14.265625730646667
- type: f1
value: 43.38565832555766
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.1923022731374213
- type: v_measure_std
value: 0.35367125475797656
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 21.872898453261598
- type: f1
value: 19.83619438998234
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 20.90506640432858
- type: f1
value: 18.92711069074263
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 29.256893073301953
- type: f1
value: 25.74578591804067
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 28.012788981800295
- type: f1
value: 25.46088957992473
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 60.886185925282355
- type: ap
value: 74.42846609840468
- type: f1
value: 59.2798285713518
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 22.47863238868196
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 20.56367396969172
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 34.6814404432133
- type: f1
value: 33.84774598794685
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 19.37246963562753
- type: f1
value: 17.143257555632257
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Xu-Ouyang/pythia-1.4b-deduped-int4-step36000-GPTQ-wikitext2
|
Xu-Ouyang
| 2024-06-28T21:05:16Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-06-28T21:04:38Z |
---
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]
|
SamagraDataGov/whisper-tiny-hi2_test
|
SamagraDataGov
| 2024-06-28T21:03:25Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-19T20:12:33Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-hi2_test
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. -->
# whisper-tiny-hi2_test
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4940
- Wer: 59.7206
## 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: 3.75e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6766 | 1.2698 | 40 | 0.6154 | 81.4733 |
| 0.3599 | 2.5397 | 80 | 0.5078 | 67.0110 |
| 0.2297 | 3.8095 | 120 | 0.4940 | 59.7206 |
| 0.153 | 5.0794 | 160 | 0.5193 | 62.0745 |
| 0.0885 | 6.3492 | 200 | 0.5557 | 60.5843 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ILKT/2024-06-22_12-37-29_epoch_13
|
ILKT
| 2024-06-28T21:00:00Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-23T07:15:10Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-22_12-37-29_epoch_13
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 21.8986083499006
- type: f1
value: 20.494045643513033
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 52.31000000000001
- type: ap
value: 14.770391705888935
- type: f1
value: 44.30460630147276
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.3467903203835494
- type: v_measure_std
value: 0.28324230950278206
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 19.024882313382648
- type: f1
value: 17.245623534419032
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 18.53910477127398
- type: f1
value: 16.574522621600035
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 26.351714862138536
- type: f1
value: 23.448065286902448
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 25.33202164289228
- type: f1
value: 23.051659134513013
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 61.10628439038517
- type: ap
value: 75.1180568321982
- type: f1
value: 59.790264880128575
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 22.646694163528075
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 20.228627260533187
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 33.9196675900277
- type: f1
value: 33.466154937936984
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 20.52631578947369
- type: f1
value: 18.122972557323955
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Omriy123/vit_epochs1_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2_test10
|
Omriy123
| 2024-06-28T20:55:22Z | 217 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-28T20:51:30Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit_epochs1_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2_test10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Dogs_vs_Cats
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.944
---
<!-- 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. -->
# vit_epochs1_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2_test10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2319
- Accuracy: 0.944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0051 | 1.0 | 469 | 0.2319 | 0.944 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.19.1
|
ekaterina-blatova-jb/model_lr1e-4_old_scheduler_with_t_max_275_all_v1
|
ekaterina-blatova-jb
| 2024-06-28T20:47:25Z | 170 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T20:45:39Z |
---
{}
---
## Evaluation results
Validation loss on the whole input: 0.8587286151014268
Validation loss on completion: 0.9442666905815713
|
Kedar84/phi-3-vision-v0.2
|
Kedar84
| 2024-06-28T20:25:15Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"phi3_v",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-06-28T20:23:25Z |
---
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]
|
ekaterina-blatova-jb/model_lr1e-4_old_scheduler_with_t_max_275_all_v0
|
ekaterina-blatova-jb
| 2024-06-28T20:24:17Z | 170 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T20:22:31Z |
---
{}
---
## Evaluation results
Validation loss on the whole input: 0.8519790729042143
Validation loss on completion: 0.9668812284362502
|
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2_test3
|
Omriy123
| 2024-06-28T20:21:04Z | 195 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-28T20:05:48Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2_test3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Dogs_vs_Cats
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9410666666666667
---
<!-- 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. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2_test3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2836
- Accuracy: 0.9411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0042 | 1.0 | 469 | 0.2944 | 0.9333 |
| 0.0389 | 2.0 | 938 | 0.2836 | 0.9411 |
| 0.0017 | 3.0 | 1407 | 0.2929 | 0.9429 |
| 0.001 | 4.0 | 1876 | 0.3287 | 0.9451 |
| 0.0001 | 5.0 | 2345 | 0.3298 | 0.9469 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.19.1
|
mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF
|
mradermacher
| 2024-06-28T20:20:53Z | 161 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO",
"base_model:quantized:chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-27T21:00:38Z |
---
base_model: chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/resolve/main/Llama-3-Instruct-8B-SimPO-ExPO.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
ILKT/2024-06-22_12-37-29_epoch_5
|
ILKT
| 2024-06-28T20:13:37Z | 147 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-22T18:32:25Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-22_12-37-29_epoch_5
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 21.361829025844926
- type: f1
value: 19.69920196782064
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 48.650000000000006
- type: ap
value: 14.2008038394895
- type: f1
value: 42.02710366782284
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.385614567474454
- type: v_measure_std
value: 0.7709471135169303
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 23.500336247478142
- type: f1
value: 21.243243108292056
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 22.96606000983768
- type: f1
value: 20.651311500662654
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 32.50168123739072
- type: f1
value: 28.748118197831474
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 31.087063453025088
- type: f1
value: 28.824301745580023
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 63.14219519258616
- type: ap
value: 75.68633293879387
- type: f1
value: 61.29650902564272
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 22.886236405259375
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 19.824200488314524
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 35.3185595567867
- type: f1
value: 35.06444726355803
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 22.08502024291498
- type: f1
value: 19.002463151657718
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Oxtiz/onnx_rubertconv_toxic_editor
|
Oxtiz
| 2024-06-28T20:05:29Z | 4 | 0 |
transformers
|
[
"transformers",
"onnx",
"bert",
"token-classification",
"ru",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-06-28T20:04:48Z |
---
language: ru
---
# Model Card for onnx_rubertconv_toxic_editor
<!-- 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:** Я
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** ru
- **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]
|
CodeHima/TOSBertV2
|
CodeHima
| 2024-06-28T20:04:19Z | 128 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"nlp",
"TOS",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-28T18:22:10Z |
---
license: mit
language:
- en
metrics:
- accuracy
widget:
- text: "You have the right to use CommunityConnect for its intended purpose of connecting with others, sharing content responsibly, and engaging in constructive dialogue. You are responsible for the content you post and must respect the rights and privacy of others."
example_title: "Fair Clause"
- text: " We reserve the right to suspend, terminate, or restrict your access to the platform at any time and for any reason, without prior notice or explanation. This includes but is not limited to violations of our community guidelines or terms of service, as determined solely by ConnectWorld."
example_title: "Unfair Clause"
library_name: transformers
pipeline_tag: text-classification
tags:
- nlp
- bert
- TOS
---
# BertTOS v2: Terms of Service Unfairness Classifier
## Model Details
- **Model Name:** BertTOS v2
- **Model Type:** Fine-tuned BERT for sequence classification
- **Version:** 2.0
- **Language(s):** English
- **License:** [MIT]
- **Developer:** [Himanshu Mohanty]
## Model Description
BertTOS v2 is a fine-tuned BERT model designed to classify clauses in Terms of Service (ToS) documents based on their unfairness level. This model can help users identify potentially problematic clauses in legal documents, particularly in the context of consumer protection.
### Task
The model performs multi-class classification on individual sentences or clauses, categorizing them into three levels of unfairness:
0. Clearly Fair
1. Potentially Unfair
2. Clearly Unfair
### Training Data
The model was trained on the [CodeHima/TOS_Dataset](https://huggingface.co/datasets/CodeHima/TOS_Dataset) dataset, which contains annotated sentences from Terms of Service documents. Each sentence is labeled with one of the three unfairness levels.
### Model Architecture
- Base Model: BERT (bert-base-uncased)
- Fine-tuning: Sequence classification head
- Input: Tokenized text (max length 512 tokens)
- Output: Probabilities for each unfairness level
## Performance
The model's performance metrics on the test set:
- Accuracy: [0.8795761078998073]
- F1 Score (weighted): [0.885282]
- Precision (weighted): [0.883729]
- Recall (weighted): [0.889157]
## Limitations
- The model is trained on English language ToS documents and may not perform well on other languages or legal contexts.
- Performance may vary depending on the specific wording and context of clauses.
- The model should be used as a tool to assist human judgment, not as a definitive legal assessment.
## Ethical Considerations
- This model is intended to help identify potentially unfair clauses, but it should not be considered as legal advice.
- Users should be aware of potential biases in the training data and model predictions.
- The model's output should be reviewed by legal professionals for critical applications.
## How to Use
You can use this model directly with the Hugging Face `transformers` library:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_name = "YourHuggingFaceUsername/TOSBertV2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Function to predict unfairness level
def predict_unfairness(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
model.eval()
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=-1).squeeze()
predicted_class = torch.argmax(probabilities).item()
label_mapping = {0: 'clearly_fair', 1: 'potentially_unfair', 2: 'clearly_unfair'}
predicted_label = label_mapping[predicted_class]
return predicted_label, probabilities.tolist()
# Example usage
clause = "The company reserves the right to change these terms at any time without notice."
predicted_label, probabilities = predict_unfairness(clause)
print(f"Predicted unfairness level: {predicted_label}")
print("Probabilities:")
for label, prob in zip(['clearly_fair', 'potentially_unfair', 'clearly_unfair'], probabilities):
print(f"{label}: {prob:.4f}")
```
## Training
The model was trained using the following hyperparameters:
- Epochs: 3
- Batch Size: 16
- Learning Rate: [ ]
- Optimizer: AdamW
- Weight Decay: 0.01
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{TOSBertV2,
author = {Himanshu Mohanty},
title = {TOSBertV2: is a fine-tuned BERT model designed to classify clauses in Terms of Service},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face Model Hub},
howpublished = {\url{https://huggingface.co/CodeHima/TOSBertV2}}
}
|
CodeHima/TOSBert
|
CodeHima
| 2024-06-28T20:03:52Z | 110 | 0 |
transformers
|
[
"transformers",
"joblib",
"safetensors",
"bert",
"text-classification",
"tos",
"terms of services",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-28T06:35:23Z |
---
language:
- en
metrics:
- accuracy
widget:
- text: "You have the right to use CommunityConnect for its intended purpose of connecting with others, sharing content responsibly, and engaging in constructive dialogue. You are responsible for the content you post and must respect the rights and privacy of others."
example_title: "Fair Clause"
- text: " We reserve the right to suspend, terminate, or restrict your access to the platform at any time and for any reason, without prior notice or explanation. This includes but is not limited to violations of our community guidelines or terms of service, as determined solely by ConnectWorld."
example_title: "Unfair Clause"
library_name: transformers
pipeline_tag: text-classification
tags:
- tos
- terms of services
- bert
---
# TOSBert
**TOSBert** is a fine-tuned BERT model for sequence classification tasks. It is trained on a custom dataset for multi-label classification.
## Model Details
- **Model Name**: TOSBert
- **Model Architecture**: BERT
- **Framework**: [Hugging Face Transformers](https://huggingface.co/transformers/)
- **Model Type**: Sequence Classification (Multi-label Classification)
## Dataset
The model is trained on the [online_terms_of_service](https://huggingface.co/datasets/joelniklaus/online_terms_of_service) dataset hosted on Hugging Face. This dataset consists of text sequences extracted from various online terms of service documents. Each sequence is labeled with multiple categories related to legal and privacy terms.
## Training
The model was fine-tuned using the following parameters:
- **Number of Epochs**: 3
- **Batch Size**: 16 (both for training and evaluation)
- **Warmup Steps**: 500
- **Weight Decay**: 0.01
- **Learning Rate**: Automatically adjusted
## Usage
### Installation
To use this model, you need to install the `transformers` library from Hugging Face:
```bash
pip install transformers
```
### Loading the Model
You can load the model using the following code:
```python
from transformers import BertForSequenceClassification, BertTokenizer
model_name = "CodeHima/TOSBert"
model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)
```
### Inference
Here is an example of how to use the model for inference:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True)
text = "Your input text here"
predictions = classifier(text)
print(predictions)
```
### Training Script
Below is an example script used for training the model:
```python
from transformers import Trainer, TrainingArguments, BertForSequenceClassification, BertTokenizer
import torch
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
# Define the model
model_name = "bert-base-uncased"
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=3)
# Define the tokenizer
tokenizer = BertTokenizer.from_pretrained(model_name)
# Load your dataset
# train_dataset and eval_dataset should be instances of torch.utils.data.Dataset
# Example: train_dataset = YourDataset(train_data)
# Define training arguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=10,
eval_strategy="epoch"
)
# Custom data collator to convert labels to floats
def data_collator(features):
batch = {}
first = features[0]
if 'label' in first and first['label'] is not None:
dtype = torch.float32
batch['labels'] = torch.tensor([f['label'] for f in features], dtype=dtype)
for k, v in first.items():
if k != 'label' and v is not None and not isinstance(v, str):
batch[k] = torch.stack([f[k] for f in features])
return batch
# Define the compute metrics function
def compute_metrics(pred):
labels = pred.label_ids
preds = (pred.predictions > 0.5).astype(int)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')
acc = accuracy_score(labels, preds)
return {
'accuracy': acc,
'f1': f1,
'precision': precision,
'recall': recall
}
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
data_collator=data_collator
)
# Train the model
trainer.train()
```
## Evaluation
To evaluate the model on the validation set, you can use the following code:
```python
results = trainer.evaluate()
print(results)
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Citation
If you use this model in your research, please cite it as follows:
```bibtex
@misc{TOSBert,
author = {Himanshu Mohanty},
title = {TOSBert: Fine-tuned BERT model for multi-label classification},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face Model Hub},
howpublished = {\url{https://huggingface.co/CodeHima/TOSBert}}
}
```
## Acknowledgements
This project uses the [Hugging Face Transformers](https://huggingface.co/transformers/) library. Special thanks to the developers and contributors of this library.
```
|
ILKT/2024-06-22_12-37-29_epoch_3
|
ILKT
| 2024-06-28T20:01:35Z | 147 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-22T15:22:02Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-22_12-37-29_epoch_3
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.77335984095427
- type: f1
value: 21.633161157413287
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 52.1
- type: ap
value: 14.669897873185539
- type: f1
value: 44.39499194571317
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.9997457724816274
- type: v_measure_std
value: 0.7798049810107266
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 23.69872225958305
- type: f1
value: 22.329202066465307
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 22.808657156910968
- type: f1
value: 21.15686015469099
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 32.39071956960323
- type: f1
value: 29.01276146175851
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 30.10821446138711
- type: f1
value: 28.129954296299786
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 59.25282363162467
- type: ap
value: 73.7417044611021
- type: f1
value: 57.76542870147847
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 23.16471941868449
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 20.457634502092688
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 36.952908587257625
- type: f1
value: 35.76600816919445
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 25.10121457489879
- type: f1
value: 20.318534993487354
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Oxtiz/onnx_rubertconv_toxic_clf
|
Oxtiz
| 2024-06-28T19:58:26Z | 4 | 0 |
transformers
|
[
"transformers",
"onnx",
"bert",
"text-classification",
"ru",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-28T19:58:04Z |
---
language: ru
---
# Model Card for onnx_rubertconv_toxic_clf
<!-- 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:** Я
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** ru
- **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]
|
ILKT/2024-06-22_12-37-29_epoch_2
|
ILKT
| 2024-06-28T19:56:14Z | 147 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-22T13:47:03Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-22_12-37-29_epoch_2
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.69383697813121
- type: f1
value: 21.362811160799673
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.339999999999996
- type: ap
value: 14.65809485813021
- type: f1
value: 45.01084326182093
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 3.5718919710260297
- type: v_measure_std
value: 0.8345651903614992
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 22.488231338264963
- type: f1
value: 20.826027786002005
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 21.898671913428434
- type: f1
value: 20.420902804885205
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 32.24277067921991
- type: f1
value: 28.185730890243683
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 30.300049188391544
- type: f1
value: 27.073486016784653
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 59.60903562119897
- type: ap
value: 74.9405933784915
- type: f1
value: 58.672915231497
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 24.760685179550322
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 23.94393594955405
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 38.91966759002769
- type: f1
value: 38.11020162091945
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 24.655870445344128
- type: f1
value: 20.860413679518636
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
koeng/output
|
koeng
| 2024-06-28T19:54:45Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T19:25:44Z |
---
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]
|
ASR-UWC/whisper-small-hi
|
ASR-UWC
| 2024-06-28T19:52:24Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-28T12:41:21Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 32.76475069838314
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4416
- Wer: 32.7648
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0919 | 2.4450 | 1000 | 0.2982 | 35.1308 |
| 0.0209 | 4.8900 | 2000 | 0.3554 | 34.1023 |
| 0.001 | 7.3350 | 3000 | 0.4183 | 32.8706 |
| 0.0005 | 9.7800 | 4000 | 0.4416 | 32.7648 |
### Framework versions
- Transformers 4.42.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF
|
gelukuMLG
| 2024-06-28T19:49:29Z | 22 | 1 | null |
[
"gguf",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-18T10:32:27Z |
---
license: llama3
---
### Compute for this merge was provided by KoboldAI.
### Important: Because this model is based on Cat-8B-Instruct-V1 it has the stop sequence issues. Make sure to add `</s>` as a stop Sequence in whatever backend or ui you are using. ###
The following models were used in this recipe:
- https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed-ft
- https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed
- https://huggingface.co/TheSkullery/llama-3-cat-8b-instruct-v1
Recipe used:
```
merge_method: passthrough
dtype: bfloat16
vocab_type: bpe
slices:
- sources:
- layer_range: [0, 24]
model: TheSkullery/llama-3-cat-8b-instruct-v1
- sources:
- layer_range: [8, 24]
model: TheSkullery/llama-3-cat-8b-instruct-v1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 24]
model: TheSkullery/llama-3-cat-8b-instruct-v1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 32]
model: TheSkullery/llama-3-cat-8b-instruct-v1
name: LLaMa-3-Cat-Instruct-Unhealed-15B
---
merge_method: task_arithmetic
dtype: bfloat16
vocab_type: bpe
base_model: elinas/Llama-3-15B-Instruct-zeroed
models:
- model: elinas/Llama-3-15B-Instruct-zeroed-ft
parameters:
weight: 1.0
- model: LLaMa-3-Cat-Instruct-Unhealed-15B
parameters:
weight: 1.0
```
|
not-lain/mayo
|
not-lain
| 2024-06-28T19:45:23Z | 116 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T19:40:16Z |
---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: mayo
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. -->
# mayo
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
shossain/gemma-test-shah
|
shossain
| 2024-06-28T19:42:50Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T18:59:44Z |
---
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]
|
yuriachermann/My_AGI_llama_2_7B
|
yuriachermann
| 2024-06-28T19:37:32Z | 3 | 2 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"Dolly",
"ipex",
"Max Series GPU",
"question-answering",
"en",
"dataset:databricks/databricks-dolly-15k",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] |
question-answering
| 2024-06-03T14:30:50Z |
---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- Dolly
- ipex
- Max Series GPU
base_model: meta-llama/Llama-2-7b-hf
datasets:
- databricks/databricks-dolly-15k
model-index:
- name: My_AGI_llama_2_7B
results: []
language:
- en
metrics:
- accuracy
- bertscore
- bleu
pipeline_tag: question-answering
---
# My_AGI_llama_2_7B
**Model Type:** Fine-Tuned
**Model Base:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
**Datasets Used:** [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
**Author:** [Yuri Achermann](https://huggingface.co/yuriachermann)
**Date:** June 03, 2024
-------------------------
## Training procedure
### Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 593
### Framework versions
- PEFT==0.11.1
- Transformers==4.41.2
- Pytorch==2.1.0.post0+cxx11.abi
- Datasets==2.19.2
- Tokenizers==0.19.1
-------------------------
## Intended uses & limitations
**Primary Use Case:** The model is intended for generating human-like responses in conversational applications, like chatbots or virtual assistants.
**Limitations:** The model may generate inaccurate or biased content as it reflects the data it was trained on. It is essential to evaluate the generated responses in context and use the model responsibly.
-------------------------
## Evaluation
The evaluation platform consists of Gaudi Accelerators and Xeon CPUs running benchmarks from the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness)
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande |
|:-------:|:-----:|:---------:|:-----:|:----------:|:----------:|
| 54.904 | 45.65 | 76.8 | 42.02 | 40.2 | 69.85 |
-------------------------
## Ethical Considerations
The model may inherit biases present in the training data. It is crucial to use the model in a way that promotes fairness and mitigates potential biases.
-------------------------
## Acknowledgments
This fine-tuning effort was made possible by the support of Intel, that provided the computing resources, and [Eduardo Alvarez](https://huggingface.co/eduardo-alvarez).
Additional shout-out to the creators of the Llama-2-7b-hf model and the contributors to the databricks-dolly-15k dataset.
-------------------------
## Contact Information
For questions or feedback about this model, please contact **[Yuri Achermann](mailto:yuri.achermann@gmail.com)**.
-------------------------
## License
This model is distributed under **Apache 2.0 License**.
|
Ananthu357/Ananthus-BAAI-for-contracts5.0
|
Ananthu357
| 2024-06-28T19:34:29Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:453",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:BAAI/bge-large-en",
"base_model:finetune:BAAI/bge-large-en",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-28T19:33:12Z |
---
base_model: BAAI/bge-large-en
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:453
- loss:CosineSimilarityLoss
widget:
- source_sentence: Termination notice
sentences:
- "having value more than Rs 20 crore and original period of completion 12 months\
\ or more, when there is no reduction in original scope of work by more than 10%,\
\ and no extension granted on either railway or Contractor\x92s account,"
- Special Conditions might exist in the contract and supersede the Standard General
Conditions.
- Subject to the provisions of the aforesaid Arbitration and Conciliation Act 1996
and the rules thereunder and relevant para of General Conditions of Contract
- source_sentence: Impact of breach of terms by subcontracting.
sentences:
- The contractor shall commence the works within 15 days after the receipt by him
of an order in wirting to this effect from the Engineer and shall proceed with
the same with due expection and without delay.
- Railway may, if satisfied that the works can be completed by the Contractor within
reasonable short time thereafter, allow the Contractor for further extension of
time (Proforma at Annexure-VII) as the Engineer may decide
- On first occasion of noticing exaggerated/ false measurement, Engineer shall recover
liquidated damages equal to 10% of claimed gross bill value.
- source_sentence: 'Place of Arbitration: The place of arbitration would be within
the geographical limits of the Division of the Railway'
sentences:
- the Railway may grant such extension or extensions of the completion date as may
be considered reasonable.
- Location for dispute resolution
- Any item of work carried out by the Contractor on the instructions of the Engineer
which is not included in the accepted Schedules of Rates shall be executed at
the rates set forth in the Schedule of Rates of Railway.
- source_sentence: Special Conditions of Contract must be referred to while
executing the contract
sentences:
- a penal interest of 12% per annum shall be charged for the delay beyond 21(Twenty
one) days, i.e. from 22nd day after the date of issue of LOA. Further, if the
60th day happens to be a declared holiday in the concerned office of the Railway,
submission of PG can be accepted on the next working day.
- Contractor should finish the works according to Special conditions of
Contract.
- This explains the impact of breaching terms in subcontracting part.
- source_sentence: Additional documents involve General Conditions of Contract, Regulations
for Tenders and Contracts and Special Conditions of Contract.
sentences:
- "At the final stage of completion and commissioning of work, in case the contractor\x92\
s failure is limited to only some of the works costing not more than 2% of the\
\ original contract value,"
- Any material found during excavation should be reported to the engineer.
- If the Contractor shall be dissatisfied by reason of any decision of the Engineer's
representative, he shall be entitled to refer the matter to the Engineer who shall
there upon confirm or vary such decision.
---
# SentenceTransformer based on BAAI/bge-large-en
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) <!-- at revision abe7d9d814b775ca171121fb03f394dc42974275 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Ananthu357/Ananthus-BAAI-for-contracts5.0")
# Run inference
sentences = [
'Additional documents involve General Conditions of Contract, Regulations for Tenders and Contracts and Special Conditions of Contract.',
"\xa0If the Contractor shall be dissatisfied by reason of any decision of the Engineer's representative, he shall be entitled to refer the matter to the Engineer who shall there upon confirm or vary such decision.",
'At the final stage of completion and commissioning of work, in case the contractor\x92s failure is limited to only some of the works costing not more than 2% of the original contract value,',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 25
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 25
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss |
|:-------:|:----:|:-------------:|:------:|
| 3.3448 | 100 | 0.06 | 0.0540 |
| 6.6897 | 200 | 0.0084 | 0.0568 |
| 10.0345 | 300 | 0.0035 | 0.0548 |
| 13.3448 | 400 | 0.0018 | 0.0536 |
| 16.6897 | 500 | 0.0011 | 0.0548 |
| 20.0345 | 600 | 0.001 | 0.0553 |
| 23.3448 | 700 | 0.0009 | 0.0556 |
| 3.3448 | 100 | 0.0014 | 0.0578 |
| 6.6897 | 200 | 0.0038 | 0.0582 |
| 10.0345 | 300 | 0.0025 | 0.0623 |
| 13.3448 | 400 | 0.0014 | 0.0579 |
| 16.6897 | 500 | 0.0008 | 0.0582 |
| 20.0345 | 600 | 0.0006 | 0.0579 |
| 23.3448 | 700 | 0.0006 | 0.0585 |
| 3.3448 | 100 | 0.0029 | 0.0640 |
| 6.6897 | 200 | 0.0048 | 0.0561 |
| 10.0345 | 300 | 0.0018 | 0.0524 |
| 13.3448 | 400 | 0.001 | 0.0522 |
| 16.6897 | 500 | 0.0007 | 0.0514 |
| 20.0345 | 600 | 0.0005 | 0.0519 |
| 23.3448 | 700 | 0.0005 | 0.0522 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
benmajor27/whisper-large-v3-hu_full
|
benmajor27
| 2024-06-28T19:29:17Z | 135 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hu",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-28T08:11:09Z |
---
base_model: openai/whisper-large-v3
datasets:
- mozilla-foundation/common_voice_17_0
language:
- hu
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Large V3 HU Full - snoopyben27
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: default
split: test
args: 'config: hu, split: test'
metrics:
- type: wer
value: 8.860932585806099
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V3 HU Full - snoopyben27
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0911
- Wer: 8.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1301 | 0.3299 | 1000 | 0.1351 | 14.5084 |
| 0.1324 | 0.6598 | 2000 | 0.1208 | 13.2777 |
| 0.1136 | 0.9898 | 3000 | 0.1066 | 11.5548 |
| 0.0471 | 1.3197 | 4000 | 0.1030 | 10.3788 |
| 0.0337 | 1.6496 | 5000 | 0.0955 | 9.8045 |
| 0.0311 | 1.9795 | 6000 | 0.0875 | 9.2438 |
| 0.0108 | 2.3095 | 7000 | 0.0911 | 8.8609 |
### Framework versions
- Transformers 4.42.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
panxinyang/Qwen-Qwen1.5-1.8B-1719602865
|
panxinyang
| 2024-06-28T19:27:48Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-28T19:27:45Z |
---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
### Framework versions
- PEFT 0.11.1
|
sert121/llama_8b_adapters
|
sert121
| 2024-06-28T19:25:02Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:defog/llama-3-sqlcoder-8b",
"base_model:adapter:defog/llama-3-sqlcoder-8b",
"region:us"
] | null | 2024-06-28T19:14:52Z |
---
base_model: defog/llama-3-sqlcoder-8b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
### Framework versions
- PEFT 0.11.1
|
Ziray/model_4_bit
|
Ziray
| 2024-06-28T19:24:31Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-28T19:12:32Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** Ziray
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ILKT/2024-06-24_00-11-56_epoch_6
|
ILKT
| 2024-06-28T19:19:44Z | 145 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-24T06:26:21Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_00-11-56_epoch_6
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.55467196819086
- type: f1
value: 19.19828737718257
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 51.33
- type: ap
value: 14.015570996047844
- type: f1
value: 43.23138599880047
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.8799530368800563
- type: v_measure_std
value: 0.256649729248376
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 18.54068594485541
- type: f1
value: 16.29550022168886
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 17.466797835710775
- type: f1
value: 14.980594904804006
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 26.36852723604573
- type: f1
value: 23.092371479862656
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 25.238563698967038
- type: f1
value: 22.644235035013953
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 55.25629887054735
- type: ap
value: 69.0709380197225
- type: f1
value: 51.947600759547306
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 27.239201733982988
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 23.144006919275732
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 37.22991689750693
- type: f1
value: 36.92338407309846
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 23.60323886639676
- type: f1
value: 19.171138843465414
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Azazelle/Llama-3-Nerdy-RP-8B
|
Azazelle
| 2024-06-28T19:19:15Z | 7 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2403.19522",
"base_model:Azazelle/ANJIR-ADAPTER-128",
"base_model:merge:Azazelle/ANJIR-ADAPTER-128",
"base_model:Azazelle/Aura_Llama3",
"base_model:merge:Azazelle/Aura_Llama3",
"base_model:Azazelle/BlueMoon_Llama3",
"base_model:merge:Azazelle/BlueMoon_Llama3",
"base_model:Azazelle/Llama-3-8B-Abomination-LORA",
"base_model:merge:Azazelle/Llama-3-8B-Abomination-LORA",
"base_model:Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B",
"base_model:merge:Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B",
"base_model:Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B",
"base_model:merge:Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B",
"base_model:Azazelle/Llama-3-LongStory-LORA",
"base_model:merge:Azazelle/Llama-3-LongStory-LORA",
"base_model:Azazelle/Llama3_RP_ORPO_LoRA",
"base_model:merge:Azazelle/Llama3_RP_ORPO_LoRA",
"base_model:Azazelle/Luna_Llama3",
"base_model:merge:Azazelle/Luna_Llama3",
"base_model:Azazelle/Nimue-8B",
"base_model:merge:Azazelle/Nimue-8B",
"base_model:Azazelle/RP_Format_QuoteAsterisk_Llama3",
"base_model:merge:Azazelle/RP_Format_QuoteAsterisk_Llama3",
"base_model:Azazelle/Smarts_Llama3",
"base_model:merge:Azazelle/Smarts_Llama3",
"base_model:Azazelle/Theory_of_Mind_Llama3",
"base_model:merge:Azazelle/Theory_of_Mind_Llama3",
"base_model:Azazelle/llama3-8b-hikikomori-v0.4",
"base_model:merge:Azazelle/llama3-8b-hikikomori-v0.4",
"base_model:ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA",
"base_model:merge:ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-28T19:05:36Z |
---
base_model:
- ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA
- Azazelle/Aura_Llama3
- Azazelle/llama3-8b-hikikomori-v0.4
- Azazelle/RP_Format_QuoteAsterisk_Llama3
- Azazelle/Theory_of_Mind_Llama3
- Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B
- Azazelle/ANJIR-ADAPTER-128
- Azazelle/Llama3_RP_ORPO_LoRA
- Azazelle/Smarts_Llama3
- Azazelle/BlueMoon_Llama3
- Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B
- Azazelle/Nimue-8B
- Azazelle/Luna_Llama3
- Azazelle/Llama-3-LongStory-LORA
- Azazelle/Llama-3-8B-Abomination-LORA
library_name: transformers
tags:
- mergekit
- merge
---
# nerdy_rp
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using output/stop_it_nerd as a base.
### Models Merged
The following models were included in the merge:
* output/stop_it_nerd + [ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA](https://huggingface.co/ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA)
* output/stop_it_nerd + [Azazelle/Aura_Llama3](https://huggingface.co/Azazelle/Aura_Llama3)
* output/stop_it_nerd + [Azazelle/llama3-8b-hikikomori-v0.4](https://huggingface.co/Azazelle/llama3-8b-hikikomori-v0.4)
* output/stop_it_nerd + [Azazelle/RP_Format_QuoteAsterisk_Llama3](https://huggingface.co/Azazelle/RP_Format_QuoteAsterisk_Llama3)
* output/stop_it_nerd + [Azazelle/Theory_of_Mind_Llama3](https://huggingface.co/Azazelle/Theory_of_Mind_Llama3)
* output/stop_it_nerd + [Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B](https://huggingface.co/Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B)
* output/stop_it_nerd + [Azazelle/ANJIR-ADAPTER-128](https://huggingface.co/Azazelle/ANJIR-ADAPTER-128)
* output/stop_it_nerd + [Azazelle/Llama3_RP_ORPO_LoRA](https://huggingface.co/Azazelle/Llama3_RP_ORPO_LoRA)
* output/stop_it_nerd + [Azazelle/Smarts_Llama3](https://huggingface.co/Azazelle/Smarts_Llama3)
* output/stop_it_nerd + [Azazelle/BlueMoon_Llama3](https://huggingface.co/Azazelle/BlueMoon_Llama3)
* output/stop_it_nerd + [Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B](https://huggingface.co/Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B)
* output/stop_it_nerd + [Azazelle/Nimue-8B](https://huggingface.co/Azazelle/Nimue-8B)
* output/stop_it_nerd + [Azazelle/Luna_Llama3](https://huggingface.co/Azazelle/Luna_Llama3)
* output/stop_it_nerd + [Azazelle/Llama-3-LongStory-LORA](https://huggingface.co/Azazelle/Llama-3-LongStory-LORA)
* output/stop_it_nerd + [Azazelle/Llama-3-8B-Abomination-LORA](https://huggingface.co/Azazelle/Llama-3-8B-Abomination-LORA)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: output/stop_it_nerd
dtype: bfloat16
merge_method: model_stock
slices:
- sources:
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Llama-3-8B-Abomination-LORA
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Llama-3-LimaRP-Instruct-LoRA-8B
- layer_range: [0, 32]
model: output/stop_it_nerd+ToastyPigeon/Llama-3-8B-Instruct-SpringDragon-V2-QLoRA
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Llama-3-LongStory-LORA
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/ANJIR-ADAPTER-128
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Llama3_RP_ORPO_LoRA
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/RP_Format_QuoteAsterisk_Llama3
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Theory_of_Mind_Llama3
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Aura_Llama3
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Luna_Llama3
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/BlueMoon_Llama3
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Smarts_Llama3
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/llama3-8b-hikikomori-v0.4
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Nimue-8B
- layer_range: [0, 32]
model: output/stop_it_nerd+Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B
- layer_range: [0, 32]
model: output/stop_it_nerd
```
|
skratos115/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF
|
skratos115
| 2024-06-28T19:15:06Z | 25 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"base_model:quantized:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-28T19:14:24Z |
---
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
license: other
license_name: deepseek-license
license_link: LICENSE
tags:
- llama-cpp
- gguf-my-repo
---
# skratos115/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo skratos115/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo skratos115/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo skratos115/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo skratos115/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M-GGUF --hf-file deepseek-coder-v2-lite-instruct-q4_k_m.gguf -c 2048
```
|
ILKT/2024-06-24_00-11-56_epoch_4
|
ILKT
| 2024-06-28T19:08:53Z | 147 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-24T03:41:06Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_00-11-56_epoch_4
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.86282306163022
- type: f1
value: 20.03845065500856
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.03
- type: ap
value: 14.035067729760556
- type: f1
value: 44.0135900331805
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.4557767917811435
- type: v_measure_std
value: 0.253061574416667
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 21.65097511768662
- type: f1
value: 20.17015022013295
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 21.431382193802264
- type: f1
value: 19.630773057041544
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 29.287155346334902
- type: f1
value: 26.364245457170743
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 28.00295130349238
- type: f1
value: 25.943766787902728
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 55.60961482768608
- type: ap
value: 69.1105053907167
- type: f1
value: 52.28026512092987
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 27.04828167882042
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 22.701215363942534
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 36.13573407202216
- type: f1
value: 34.79457801402416
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 17.65182186234818
- type: f1
value: 16.40280459706257
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
mradermacher/cosmosage-v3-GGUF
|
mradermacher
| 2024-06-28T19:07:43Z | 103 | 0 |
transformers
|
[
"transformers",
"gguf",
"physics",
"cosmology",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:Tijmen2/cosmosage-v3",
"base_model:quantized:Tijmen2/cosmosage-v3",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-28T16:42:33Z |
---
base_model: Tijmen2/cosmosage-v3
datasets:
- teknium/OpenHermes-2.5
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- physics
- cosmology
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Tijmen2/cosmosage-v3
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/cosmosage-v3-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/cosmosage-v3-GGUF/resolve/main/cosmosage-v3.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
neo-tax/technical-in-nature-classifier-for-projects
|
neo-tax
| 2024-06-28T19:07:14Z | 97 | 0 |
setfit
|
[
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:avsolatorio/GIST-Embedding-v0",
"base_model:finetune:avsolatorio/GIST-Embedding-v0",
"region:us"
] |
text-classification
| 2024-06-28T19:06:43Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: avsolatorio/GIST-Embedding-v0
metrics:
- accuracy
widget:
- text: The project is focused on developing a new employee benefits package designed
to attract and retain top talent. We will conduct competitive benchmarking to
understand industry standards, gather employee feedback to identify desired benefits,
and create a comprehensive package that includes health, wellness, and financial
incentives.
- text: A tire manufacturing company created a new belt to be used as part of tread
cooling during the manufacturing process. Such a belt is not commercially available.
- text: Covers tasks related to data quality and compliance. This includes handling
data errors, updating data catalog definitions, and implementing compliance updates.
The project aims to ensure the accuracy, completeness, and compliance of the company's
data, thereby increasing its reliability and trustworthiness.
- text: Involves the development, testing, and maintenance of the Huntress agent software.
This includes fixing bugs, improving error handling, and adding new functionalities.
The project ensures the agent software is reliable and effective in protecting
customer systems.
- text: This project involved integrating an off-the-shelf software program into the
company's existing software infrastructure with the goal of improving the customer
data allocation and retention processes. The design and development of the integrations
required to succesfully launch the program within the company's existing software
architecture required the Python programming language. This development required
the performance of siginificant testing in an iterative nature by the development
team because Python had never been used to integrate applications within the company's
platform previously.
pipeline_tag: text-classification
inference: true
---
# SetFit with avsolatorio/GIST-Embedding-v0
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [avsolatorio/GIST-Embedding-v0](https://huggingface.co/avsolatorio/GIST-Embedding-v0) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [avsolatorio/GIST-Embedding-v0](https://huggingface.co/avsolatorio/GIST-Embedding-v0)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>"A manufacturing corporation undertakes an initiative to restructure its manufacturing organization by designing an organizational structure that will improve the company's business operations"</li><li>"Centers on the production of content for the Brief product. This includes tasks related to drafting insights, creating case studies, and publishing social media posts. The project aims to provide valuable and timely information to Kharon's clients, helping them stay informed about global security topics that impact their commercial activities."</li><li>'The team is developing a comprehensive marketing strategy to increase brand awareness and customer engagement. This includes creating targeted advertising campaigns, optimizing our social media presence, and collaborating with influencers to promote our products. We will also analyze market trends and consumer behavior to refine our approach.'</li></ul> |
| 1 | <ul><li>"Project focused on enhancing the website's functionality, including tasks related to optimizing search functionality and integrating new features such as bookmarks and table of contents for the web reader. The project aims to provide a seamless online experience for customers by improving the efficiency and speed of our website."</li><li>'Design and create an innovative drug delivery system for cancer treatment compatible with different types of cancer and different patient profiles while minimizing negative impacts on healthy tissues'</li><li>'Develop a new and advanced Natural Language Processing (NLP) algorithm to enhance the capabilities of virtual assistants used in various applications, such as customer service chatbots. This project involved improving the NLP algorithm to be more responsive in the area of complex natural language understanding, including context comprehension, sentiment analysis, and accurate response generation'</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("A tire manufacturing company created a new belt to be used as part of tread cooling during the manufacturing process. Such a belt is not commercially available.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 23 | 43.5 | 54 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 8 |
| 1 | 16 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (0.0001, 0.0001)
- head_learning_rate: 0.0001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0167 | 1 | 0.2764 | - |
| 0.8333 | 50 | 0.0014 | - |
| 1.6667 | 100 | 0.0011 | - |
| 2.5 | 150 | 0.0011 | - |
### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1
- Datasets: 2.19.2
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Niggendar/edgAnzhc_aaaaanzhcpower
|
Niggendar
| 2024-06-28T19:06:30Z | 140 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-06-28T19:02:51Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ILKT/2024-06-24_00-11-56_epoch_3
|
ILKT
| 2024-06-28T19:03:26Z | 148 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-24T02:18:49Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_00-11-56_epoch_3
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.693836978131216
- type: f1
value: 19.7785246426658
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.059999999999995
- type: ap
value: 14.572523115281122
- type: f1
value: 44.666538368681046
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 2.6012385482248446
- type: v_measure_std
value: 0.5829268652344415
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 20.077336919973103
- type: f1
value: 19.816169753103157
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 19.660600098376783
- type: f1
value: 19.188722526724238
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 29.176193678547406
- type: f1
value: 26.376493006806534
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 28.411214953271024
- type: f1
value: 26.216871325994344
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 56.13669273095858
- type: ap
value: 70.01898255793628
- type: f1
value: 53.68729025101361
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 26.850340797882478
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 23.162374198242148
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 37.90858725761773
- type: f1
value: 38.70869305063897
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 25.647773279352226
- type: f1
value: 20.54090952680169
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
ILKT/2024-06-24_00-11-56_epoch_2
|
ILKT
| 2024-06-28T18:56:46Z | 143 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-24T00:56:41Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_00-11-56_epoch_2
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 20.636182902584494
- type: f1
value: 18.970548449520848
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.260000000000005
- type: ap
value: 13.42046897399368
- type: f1
value: 43.45180723241649
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 3.1377802940926958
- type: v_measure_std
value: 0.33155306924832717
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 17.70006724949563
- type: f1
value: 16.72072580681421
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 17.220855878012788
- type: f1
value: 16.107122172246818
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 26.37188971082717
- type: f1
value: 23.0257457094473
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 24.530250860796855
- type: f1
value: 22.097320507641246
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 53.35360556038226
- type: ap
value: 69.100142615254
- type: f1
value: 51.20380111249444
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 25.985522577017615
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 22.75038559862368
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 31.77285318559557
- type: f1
value: 31.517754047233744
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 24.51417004048583
- type: f1
value: 19.865669742797284
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Makkoen/whisper-large-cit-synth-do0.15-wd0-lr1e-05-1000
|
Makkoen
| 2024-06-28T18:46:51Z | 13 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-28T14:57:16Z |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ./whisper-large-cit-synth-do0.15-wd0-lr1e-05-1000
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. -->
# ./whisper-large-cit-synth-do0.15-wd0-lr1e-05-1000
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the SF 1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4507
- Wer: 21.8713
## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6733 | 0.3556 | 20 | 0.4937 | 29.2008 |
| 0.4835 | 0.7111 | 40 | 0.3850 | 24.2105 |
| 0.4129 | 1.0667 | 60 | 0.3589 | 23.5088 |
| 0.268 | 1.4222 | 80 | 0.3472 | 23.4698 |
| 0.2525 | 1.7778 | 100 | 0.3474 | 23.0019 |
| 0.1903 | 2.1333 | 120 | 0.3608 | 22.7680 |
| 0.1316 | 2.4889 | 140 | 0.3730 | 22.9240 |
| 0.1368 | 2.8444 | 160 | 0.3545 | 25.3801 |
| 0.088 | 3.2 | 180 | 0.3879 | 23.0409 |
| 0.0688 | 3.5556 | 200 | 0.4038 | 23.9376 |
| 0.0672 | 3.9111 | 220 | 0.3813 | 22.1832 |
| 0.0449 | 4.2667 | 240 | 0.4250 | 22.8070 |
| 0.0338 | 4.6222 | 260 | 0.4314 | 22.2222 |
| 0.0376 | 4.9778 | 280 | 0.4250 | 21.4425 |
| 0.0183 | 5.3333 | 300 | 0.4507 | 21.8713 |
### Framework versions
- Transformers 4.42.3
- Pytorch 1.13.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|
ILKT/2024-06-24_22-31-18_epoch_75
|
ILKT
| 2024-06-28T18:45:37Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T20:22:20Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_75
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 24.145129224652084
- type: f1
value: 22.31539517311173
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.90999999999999
- type: ap
value: 15.1506532658194
- type: f1
value: 45.64169846891563
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 7.835526691746403
- type: v_measure_std
value: 1.310069183656216
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 29.41492938802959
- type: f1
value: 26.91718168750773
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 28.504672897196258
- type: f1
value: 25.757449612360034
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 37.96234028244788
- type: f1
value: 36.05116062969537
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 36.827348745696014
- type: f1
value: 35.7078301081846
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 64.63944396177237
- type: ap
value: 73.05639300191305
- type: f1
value: 60.4690982645747
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 35.910590406842516
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 31.836353910360828
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 49.37673130193907
- type: f1
value: 50.27096396342048
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 25.000000000000007
- type: f1
value: 20.950348494099522
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
ILKT/2024-06-24_22-31-18_epoch_74
|
ILKT
| 2024-06-28T18:44:07Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T20:03:06Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_74
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 24.67196819085487
- type: f1
value: 22.855737534484867
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.74
- type: ap
value: 15.0141225254739
- type: f1
value: 45.236014313387614
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 10.61107724605009
- type: v_measure_std
value: 2.2605034803236417
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 30.15131136516477
- type: f1
value: 27.89887698215734
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 29.41957697983276
- type: f1
value: 27.06886156146294
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 39.84196368527236
- type: f1
value: 37.97975771209762
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 38.89326119035908
- type: f1
value: 37.72354573779029
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 64.47726614538082
- type: ap
value: 72.9770994253305
- type: f1
value: 60.30210466691275
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.87977871116745
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 32.53310014460228
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 48.1578947368421
- type: f1
value: 49.212713621346325
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 26.396761133603242
- type: f1
value: 21.867946399471073
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
ILKT/2024-06-24_22-31-18_epoch_73
|
ILKT
| 2024-06-28T18:42:52Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T19:43:45Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_73
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 23.856858846918488
- type: f1
value: 21.80615781911542
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 54.059999999999995
- type: ap
value: 14.833986910689228
- type: f1
value: 45.19798171686985
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 10.443779071971166
- type: v_measure_std
value: 1.189184499084186
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 27.37054472091459
- type: f1
value: 25.346968967159466
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 26.994589276930643
- type: f1
value: 24.627788712160477
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 36.809011432414266
- type: f1
value: 35.05451356484148
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 36.16330545991146
- type: f1
value: 34.814635777103135
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 64.06892557196639
- type: ap
value: 72.8523138548515
- type: f1
value: 59.70619041148899
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.764013746251244
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 32.38294030377355
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 44.653739612188375
- type: f1
value: 46.82810070789533
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 25.020242914979757
- type: f1
value: 21.77577046612537
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
Sahab55/Conv_text_summarization_BART
|
Sahab55
| 2024-06-28T18:42:00Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-06-28T18:40:05Z |
---
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]
|
migaraa/lora_phi-1_5
|
migaraa
| 2024-06-28T18:41:32Z | 2 | 3 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dolly",
"ipex",
"max series gpu",
"dataset:generator",
"base_model:microsoft/phi-1_5",
"base_model:adapter:microsoft/phi-1_5",
"license:mit",
"region:us"
] | null | 2024-05-31T17:24:57Z |
---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- dolly
- ipex
- max series gpu
base_model: microsoft/phi-1_5
datasets:
- generator
model-index:
- name: lora_phi-1_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. -->
# lora_phi-1_5
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3998
## Model description
This is a fine-tuned version of the [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) model using Parameter Efficient Fine Tuning (PEFT) with Low Rank Adaptation (LoRA) on Intel(R) Data Center GPU Max 1100 and Intel(R) Xeon(R) Platinum 8480+ CPU .
This model can be used for various text generation tasks including chatbots, content creation, and other NLP applications.
## Training Hardware
This model was trained using: GPU:
- Intel(R) Data Center GPU Max 1100
- CPU: Intel(R) Xeon(R) Platinum 8480+
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 593
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.7756 | 0.8065 | 100 | 2.5791 |
| 2.558 | 1.6129 | 200 | 2.4656 |
| 2.4521 | 2.4194 | 300 | 2.4294 |
| 2.4589 | 3.2258 | 400 | 2.4103 |
| 2.4248 | 4.0323 | 500 | 2.3998 |
## Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ILKT/2024-06-24_22-31-18_epoch_72
|
ILKT
| 2024-06-28T18:37:05Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T19:24:21Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_72
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.087475149105362
- type: f1
value: 19.5706382319453
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 55.00000000000001
- type: ap
value: 15.36881518626701
- type: f1
value: 46.06383006480823
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 8.405906184566783
- type: v_measure_std
value: 0.8490983655584128
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 27.895090786819104
- type: f1
value: 25.724339315547738
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 27.437284800787015
- type: f1
value: 24.950468469212687
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 35.4808338937458
- type: f1
value: 33.778222802971214
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 35.164781111657646
- type: f1
value: 33.81576557802537
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 61.8853171155517
- type: ap
value: 71.88997519583735
- type: f1
value: 58.02908285755359
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.42437672413899
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 32.50435792527687
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 47.75623268698062
- type: f1
value: 48.27530003115992
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 24.433198380566797
- type: f1
value: 20.50184978405958
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
skratos115/qwen2-7b-OpenDevin-f16
|
skratos115
| 2024-06-28T18:35:39Z | 7 | 0 | null |
[
"gguf",
"text-generation",
"qwen2",
"instruct",
"unsloth",
"OpenDevin",
"dataset:xingyaoww/opendevin-code-act",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-27T21:16:33Z |
---
license: mit
tags:
- text-generation
- qwen2
- instruct
- unsloth
- OpenDevin
datasets:
- xingyaoww/opendevin-code-act
---
## Qwen2.7b.OpenDevin
brought to you by skratos115 (HF) / Kingatlas115 (GH) in colaboration with the official Opendevin Team ~xingyaoww
# Qwen2-7B-Instruct with OpenDevin Tool Calling
## Overview
This project involves the fine-tuning of the `Qwen2-7B-Instruct` model using the [opendevin-code-act dataset](https://huggingface.co/datasets/xingyaoww/opendevin-code-act) with the help of Unsloth. The primary goal is to develop a more powerful LLM capable of effectively using the CodeAct framework for tool calling. This is still in early development and should not be used in production. We are working on building a bigger dataset for tool paths/ trajectories and could you all the help we can by using the feedback integration to help us build better trajectories and release to the public via MIT license for OSS model training.
read more here:https://x.com/gneubig/status/1802740786242420896 and http://www.linkedin.com/feed/update/urn:li:activity:7208507606728929280/
## Model Details
- **Model Name**: Qwen2-7B-Instruct
- **Dataset**: [opendevin-code-act](https://huggingface.co/datasets/xingyaoww/opendevin-code-act)
- **Training Platform**: Unsloth
provided full merged files
or
Quantized f16, q4_k_m, Q5_k_m, and Q8_0 gguf files.
I used the qwen2.7b.OD.q4_k_m.gguf for my testing and got it to write me a simple script. more testing to come.
## Running the Model
You can run this model using `vLLM` or `ollama`. The following instructions are for using `ollama`.
### Prerequisites
- Docker
- Hugging Face `transformers` library (version >= 4.37.0 is recommended)
q-4k
ollama run skratos115/qwen2-7b-opendevin-q4_k_m
or
f16
ollama run skratos115/qwen2-7b-opendevin-f16
### Running with Ollama
1. **Install Docker**: Ensure you have Docker installed on your machine.
2. **Pull the Latest Hugging Face Transformers**:
pip install transformers>=4.37.0
3. **Set Up Your Workspace**:
WORKSPACE_BASE=$(pwd)/workspace
4. **Run the Docker Command**:
docker run -it \
--pull=always \
-e SANDBOX_USER_ID=$(id -u) \
-e PERSIST_SANDBOX="true" \
-e LLM_API_KEY="ollama" \
-e LLM_BASE_URL="http://[yourIPhere or 0.0.0.0]:11434" \
-e SSH_PASSWORD="make something up here" \
-e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
-v $WORKSPACE_BASE:/opt/workspace_base \
-v /var/run/docker.sock:/var/run/docker.sock \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name opendevin-app-$(date +%Y%m%d%H%M%S) \
ghcr.io/opendevin/opendevin:main
Replace `[yourIPhere or 0.0.0.0]` with your actual IP address or use `0.0.0.0` for localhost.
## Early Development
This project is in its early stages, and we are continuously working to improve the model and its capabilities. Contributions and feedback are welcome.
## Support my work
Right now all of my work has been funded personally, if you like my work and can help support growth in the AI community consider joining or donating to my Patreon.
[Patreon Link](https://www.patreon.com/atlasaisecurity)
## License
This project is licensed under the [MIT License](LICENSE).
|
skratos115/qwen2-7b-OpenDevin-q5_k_m
|
skratos115
| 2024-06-28T18:27:22Z | 4 | 0 | null |
[
"gguf",
"text-generation",
"qwen2",
"instruct",
"unsloth",
"OpenDevin",
"dataset:xingyaoww/opendevin-code-act",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-27T21:27:38Z |
---
license: mit
tags:
- text-generation
- qwen2
- instruct
- unsloth
- OpenDevin
datasets:
- xingyaoww/opendevin-code-act
---
## Qwen2.7b.OpenDevin
brought to you by skratos115 (HF) / Kingatlas115 (GH) in colaboration with the official Opendevin Team ~xingyaoww
# Qwen2-7B-Instruct with OpenDevin Tool Calling
## Overview
This project involves the fine-tuning of the `Qwen2-7B-Instruct` model using the [opendevin-code-act dataset](https://huggingface.co/datasets/xingyaoww/opendevin-code-act) with the help of Unsloth. The primary goal is to develop a more powerful LLM capable of effectively using the CodeAct framework for tool calling. This is still in early development and should not be used in production. We are working on building a bigger dataset for tool paths/ trajectories and could you all the help we can by using the feedback integration to help us build better trajectories and release to the public via MIT license for OSS model training.
read more here:https://x.com/gneubig/status/1802740786242420896 and http://www.linkedin.com/feed/update/urn:li:activity:7208507606728929280/
## Model Details
- **Model Name**: Qwen2-7B-Instruct
- **Dataset**: [opendevin-code-act](https://huggingface.co/datasets/xingyaoww/opendevin-code-act)
- **Training Platform**: Unsloth
provided full merged files
or
Quantized f16, q4_k_m, Q5_k_m, and Q8_0 gguf files.
I used the qwen2.7b.OD.q4_k_m.gguf for my testing and got it to write me a simple script. more testing to come.
## Running the Model
You can run this model using `vLLM` or `ollama`. The following instructions are for using `ollama`.
### Prerequisites
- Docker
- Hugging Face `transformers` library (version >= 4.37.0 is recommended)
### Running with Ollama
1. **Install Docker**: Ensure you have Docker installed on your machine.
2. **Pull the Latest Hugging Face Transformers**:
pip install transformers>=4.37.0
3. **Set Up Your Workspace**:
WORKSPACE_BASE=$(pwd)/workspace
4. **Run the Docker Command**:
docker run -it \
--pull=always \
-e SANDBOX_USER_ID=$(id -u) \
-e PERSIST_SANDBOX="true" \
-e LLM_API_KEY="ollama" \
-e LLM_BASE_URL="http://[yourIPhere or 0.0.0.0]:11434" \
-e SSH_PASSWORD="make something up here" \
-e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
-v $WORKSPACE_BASE:/opt/workspace_base \
-v /var/run/docker.sock:/var/run/docker.sock \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name opendevin-app-$(date +%Y%m%d%H%M%S) \
ghcr.io/opendevin/opendevin:main
Replace `[yourIPhere or 0.0.0.0]` with your actual IP address or use `0.0.0.0` for localhost.
## Early Development
This project is in its early stages, and we are continuously working to improve the model and its capabilities. Contributions and feedback are welcome.
## Support my work
Right now all of my work has been funded personally, if you like my work and can help support growth in the AI community consider joining or donating to my Patreon.
[Patreon Link](https://www.patreon.com/atlasaisecurity)
## License
This project is licensed under the [MIT License](LICENSE).
|
ILKT/2024-06-24_22-31-18_epoch_70
|
ILKT
| 2024-06-28T18:26:45Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T18:45:06Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_70
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 24.115308151093434
- type: f1
value: 21.80844479100129
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 54.96
- type: ap
value: 15.975489143022825
- type: f1
value: 46.83152716570406
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 10.734363928408763
- type: v_measure_std
value: 2.116834644117752
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 29.620040349697373
- type: f1
value: 27.769851853273774
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 28.8047220855878
- type: f1
value: 26.228250502335253
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 37.46133154001345
- type: f1
value: 35.909950764169
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 37.486473192326606
- type: f1
value: 36.20974947505478
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 61.89110918042282
- type: ap
value: 72.10070914897945
- type: f1
value: 57.89426378304563
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.15342836510209
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 32.24500312313666
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 49.01662049861496
- type: f1
value: 50.49138745910867
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 26.7004048582996
- type: f1
value: 20.54151599200167
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
skratos115/qwen2-7b-OpenDevin-q8_o
|
skratos115
| 2024-06-28T18:25:57Z | 6 | 0 | null |
[
"gguf",
"text-generation",
"qwen2",
"instruct",
"unsloth",
"OpenDevin",
"dataset:xingyaoww/opendevin-code-act",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-27T21:48:18Z |
---
license: mit
tags:
- text-generation
- qwen2
- instruct
- unsloth
- OpenDevin
datasets:
- xingyaoww/opendevin-code-act
---
## Qwen2.7b.OpenDevin
brought to you by skratos115 (HF) / Kingatlas115 (GH) in colaboration with the official Opendevin Team ~xingyaoww
# Qwen2-7B-Instruct with OpenDevin Tool Calling
## Overview
This project involves the fine-tuning of the `Qwen2-7B-Instruct` model using the [opendevin-code-act dataset](https://huggingface.co/datasets/xingyaoww/opendevin-code-act) with the help of Unsloth. The primary goal is to develop a more powerful LLM capable of effectively using the CodeAct framework for tool calling. This is still in early development and should not be used in production. We are working on building a bigger dataset for tool paths/ trajectories and could you all the help we can by using the feedback integration to help us build better trajectories and release to the public via MIT license for OSS model training.
read more here:https://x.com/gneubig/status/1802740786242420896 and http://www.linkedin.com/feed/update/urn:li:activity:7208507606728929280/
## Model Details
- **Model Name**: Qwen2-7B-Instruct
- **Dataset**: [opendevin-code-act](https://huggingface.co/datasets/xingyaoww/opendevin-code-act)
- **Training Platform**: Unsloth
provided full merged files
or
Quantized f16, q4_k_m, Q5_k_m, and Q8_0 gguf files.
I used the qwen2.7b.OD.q4_k_m.gguf for my testing and got it to write me a simple script. more testing to come.
## Running the Model
You can run this model using `vLLM` or `ollama`. The following instructions are for using `ollama`.
### Prerequisites
- Docker
- Hugging Face `transformers` library (version >= 4.37.0 is recommended)
### Running with Ollama
1. **Install Docker**: Ensure you have Docker installed on your machine.
2. **Pull the Latest Hugging Face Transformers**:
pip install transformers>=4.37.0
3. **Set Up Your Workspace**:
WORKSPACE_BASE=$(pwd)/workspace
4. **Run the Docker Command**:
docker run -it \
--pull=always \
-e SANDBOX_USER_ID=$(id -u) \
-e PERSIST_SANDBOX="true" \
-e LLM_API_KEY="ollama" \
-e LLM_BASE_URL="http://[yourIPhere or 0.0.0.0]:11434" \
-e SSH_PASSWORD="make something up here" \
-e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
-v $WORKSPACE_BASE:/opt/workspace_base \
-v /var/run/docker.sock:/var/run/docker.sock \
-p 3000:3000 \
--add-host host.docker.internal:host-gateway \
--name opendevin-app-$(date +%Y%m%d%H%M%S) \
ghcr.io/opendevin/opendevin:main
Replace `[yourIPhere or 0.0.0.0]` with your actual IP address or use `0.0.0.0` for localhost.
## Early Development
This project is in its early stages, and we are continuously working to improve the model and its capabilities. Contributions and feedback are welcome.
## Support my work
Right now all of my work has been funded personally, if you like my work and can help support growth in the AI community consider joining or donating to my Patreon.
[Patreon Link](https://www.patreon.com/atlasaisecurity)
## License
This project is licensed under the [MIT License](LICENSE).
|
ILKT/2024-06-24_22-31-18_epoch_69
|
ILKT
| 2024-06-28T18:25:35Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T18:25:58Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_69
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 23.807157057654074
- type: f1
value: 20.74866830583465
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.43
- type: ap
value: 15.370300273373735
- type: f1
value: 45.485816633592535
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 8.34157848929292
- type: v_measure_std
value: 1.6835064788653904
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 29.852051109616678
- type: f1
value: 27.65059576149131
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 29.729463846532223
- type: f1
value: 26.742962510648756
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 38.97108271687962
- type: f1
value: 37.044830848927745
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 38.780127889818004
- type: f1
value: 37.32570314592107
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 61.1120764552563
- type: ap
value: 72.05719264653985
- type: f1
value: 57.694837317259505
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.074828238780164
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 31.834564414565435
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 46.10803324099724
- type: f1
value: 46.81820320119227
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 22.08502024291498
- type: f1
value: 19.404680394030223
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
srinivasan-sridhar28/distilbert-base-uncased-finetuned-imdb
|
srinivasan-sridhar28
| 2024-06-28T18:24:08Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"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"
] |
fill-mask
| 2024-06-28T18:08:56Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
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: 2.3472
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8573 | 1.0 | 157 | 2.4537 |
| 2.0705 | 2.0 | 314 | 2.4086 |
| 2.2841 | 3.0 | 471 | 2.4206 |
| 2.4046 | 4.0 | 628 | 2.3390 |
| 2.3871 | 5.0 | 785 | 2.3809 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
AIEKEK/xlm-roberta-base-finetuned-panx-de
|
AIEKEK
| 2024-06-28T18:17:24Z | 104 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-06-28T16:58:10Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
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. -->
# xlm-roberta-base-finetuned-panx-de
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: 0.1336
- F1: 0.8593
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2517 | 1.0 | 525 | 0.1447 | 0.8336 |
| 0.1277 | 2.0 | 1050 | 0.1397 | 0.8476 |
| 0.0818 | 3.0 | 1575 | 0.1336 | 0.8593 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.16.1
- Tokenizers 0.15.2
|
ILKT/2024-06-24_22-31-18_epoch_67
|
ILKT
| 2024-06-28T18:14:57Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T17:48:37Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_67
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 24.622266401590455
- type: f1
value: 22.936267682156487
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.48
- type: ap
value: 15.322095521539064
- type: f1
value: 45.49225512083147
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 9.363928383066206
- type: v_measure_std
value: 1.3367977820048715
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 26.54001344989913
- type: f1
value: 23.96832609186341
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 26.015740285292676
- type: f1
value: 23.212345772348385
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 34.862138533960994
- type: f1
value: 32.8318592868999
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 34.63354648303001
- type: f1
value: 33.231436557685505
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 65.54590211410367
- type: ap
value: 74.21876513105504
- type: f1
value: 62.16874555498553
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 35.760616638633856
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 32.24926171089566
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 49.279778393351805
- type: f1
value: 49.51142756516184
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 18.157894736842103
- type: f1
value: 15.771804883173445
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
ILKT/2024-06-24_22-31-18_epoch_65
|
ILKT
| 2024-06-28T18:04:30Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T17:09:49Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_65
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 24.284294234592448
- type: f1
value: 22.277413998996426
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 53.23
- type: ap
value: 14.760186188077112
- type: f1
value: 44.877577191437354
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 7.492921935218096
- type: v_measure_std
value: 1.1544515147427612
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 28.449899125756563
- type: f1
value: 26.206394858233033
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 28.224299065420567
- type: f1
value: 25.556535309581264
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 34.77135171486214
- type: f1
value: 33.58898828363504
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 34.79094933595671
- type: f1
value: 33.86935454255312
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 61.5059368664929
- type: ap
value: 72.39105631460139
- type: f1
value: 58.287677162199735
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.2986850948915
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 31.6682523752145
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 49.73684210526316
- type: f1
value: 50.66885015512346
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 24.47368421052632
- type: f1
value: 20.53223695541805
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf
|
RichardErkhov
| 2024-06-28T18:04:15Z | 79 | 0 | null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-28T15:24:54Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
OrpoLlama-3-8B-memorize-translate - GGUF
- Model creator: https://huggingface.co/ItchyChin/
- Original model: https://huggingface.co/ItchyChin/OrpoLlama-3-8B-memorize-translate/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [OrpoLlama-3-8B-memorize-translate.Q2_K.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q2_K.gguf) | Q2_K | 2.96GB |
| [OrpoLlama-3-8B-memorize-translate.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [OrpoLlama-3-8B-memorize-translate.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [OrpoLlama-3-8B-memorize-translate.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [OrpoLlama-3-8B-memorize-translate.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [OrpoLlama-3-8B-memorize-translate.Q3_K.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q3_K.gguf) | Q3_K | 3.74GB |
| [OrpoLlama-3-8B-memorize-translate.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [OrpoLlama-3-8B-memorize-translate.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [OrpoLlama-3-8B-memorize-translate.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [OrpoLlama-3-8B-memorize-translate.Q4_0.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q4_0.gguf) | Q4_0 | 4.34GB |
| [OrpoLlama-3-8B-memorize-translate.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [OrpoLlama-3-8B-memorize-translate.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [OrpoLlama-3-8B-memorize-translate.Q4_K.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q4_K.gguf) | Q4_K | 4.58GB |
| [OrpoLlama-3-8B-memorize-translate.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [OrpoLlama-3-8B-memorize-translate.Q4_1.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q4_1.gguf) | Q4_1 | 4.78GB |
| [OrpoLlama-3-8B-memorize-translate.Q5_0.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q5_0.gguf) | Q5_0 | 5.21GB |
| [OrpoLlama-3-8B-memorize-translate.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [OrpoLlama-3-8B-memorize-translate.Q5_K.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q5_K.gguf) | Q5_K | 5.34GB |
| [OrpoLlama-3-8B-memorize-translate.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [OrpoLlama-3-8B-memorize-translate.Q5_1.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q5_1.gguf) | Q5_1 | 5.65GB |
| [OrpoLlama-3-8B-memorize-translate.Q6_K.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q6_K.gguf) | Q6_K | 6.14GB |
| [OrpoLlama-3-8B-memorize-translate.Q8_0.gguf](https://huggingface.co/RichardErkhov/ItchyChin_-_OrpoLlama-3-8B-memorize-translate-gguf/blob/main/OrpoLlama-3-8B-memorize-translate.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Niggendar/pixelpaintBeautiful_pony
|
Niggendar
| 2024-06-28T18:02:54Z | 151 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-06-28T17:55:54Z |
---
library_name: diffusers
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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|
ILKT/2024-06-24_22-31-18_epoch_63
|
ILKT
| 2024-06-28T17:57:51Z | 140 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-06-25T16:31:16Z |
---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_63
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 23.578528827037776
- type: f1
value: 20.90009720694687
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 52.459999999999994
- type: ap
value: 14.87705319359566
- type: f1
value: 44.71010123669066
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 7.928127147660552
- type: v_measure_std
value: 1.6645763520468402
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 23.67182246133154
- type: f1
value: 20.548969166047907
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 23.664535169699953
- type: f1
value: 20.718400089026655
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 30.689307330195025
- type: f1
value: 28.88911729338991
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 30.34923757993114
- type: f1
value: 29.18785219509149
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 59.78858963220388
- type: ap
value: 71.45585761021971
- type: f1
value: 56.6172686091966
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.03293025208843
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 31.642275273328757
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 46.551246537396125
- type: f1
value: 47.86798958676618
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 18.927125506072876
- type: f1
value: 17.117804408762236
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
mlx-community/Hercules-5.0-Qwen2-1.5B-8bits
|
mlx-community
| 2024-06-28T17:53:16Z | 7 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"en",
"dataset:Locutusque/hercules-v5.0",
"license:apache-2.0",
"region:us"
] | null | 2024-06-28T17:37:40Z |
---
language:
- en
license: apache-2.0
tags:
- mlx
datasets:
- Locutusque/hercules-v5.0
inference:
parameters:
do_sample: true
temperature: 0.8
top_p: 0.95
top_k: 40
min_p: 0.1
max_new_tokens: 250
repetition_penalty: 1.1
---
# mlx-community/Hercules-5.0-Qwen2-1.5B-8bits
The Model [mlx-community/Hercules-5.0-Qwen2-1.5B-8bits](https://huggingface.co/mlx-community/Hercules-5.0-Qwen2-1.5B-8bits) was converted to MLX format from [M4-ai/Hercules-5.0-Qwen2-1.5B](https://huggingface.co/M4-ai/Hercules-5.0-Qwen2-1.5B) using mlx-lm version **0.14.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Hercules-5.0-Qwen2-1.5B-8bits")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
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