# Model Card for kz919/mistral-7b-dpo-open-orca-flan-50k-synthetic-5-models

## Model Description

The `kz919/mistral-7b-dpo-open-orca-flan-50k-synthetic-5-models` is a text generation model designed for high-quality language generation tasks. This model has been trained using a unique DPO approach, contrasting ground truth completions with synthetic responses generated by five different models: [Ignos-Mistral-T5-7B-v1](https://huggingface.co/ignos/Mistral-T5-7B-v1), [cognAI-lil-c3po](https://huggingface.co/cognAI/lil-c3po), [viethq188-Rabbit-7B-DPO-Chat](https://huggingface.co/viethq188/Rabbit-7B-DPO-Chat), [cookinai-DonutLM-v1](cookinai/DonutLM-v1), and [v1olet-v1olet-merged-dpo-7B](https://huggingface.co/v1olet/v1olet_merged_dpo_7B). The training methodology is inspired by the `stack_llama_2` script, which can be found in the TRL (Transformers Reinforcement Learning) repository of Hugging Face.

### Intended Use

This model is intended for a variety of text generation tasks where high-quality, coherent, and contextually relevant language generation is required. It is particularly suited for applications in chatbots, conversational agents, content creation, and any other scenario where nuanced and sophisticated language generation is beneficial.

### Training Data

The model was trained on the [kz919/open-orca-flan-50k-synthetic-5-models](https://huggingface.co/datasets/kz919/open-orca-flan-50k-synthetic-5-models) dataset. This dataset consists of a diverse range of text, designed to provide a comprehensive foundation for the model's language understanding and generation capabilities.

### Training Procedure

The training followed a novel procedure modeled after the `stack_llama_2` script, utilizing a reinforcement learning framework. In this approach, the model was fine-tuned by contrasting ground truth completions with synthetic responses generated by the aforementioned five models. This method allowed the model to learn a variety of linguistic styles and nuances, enhancing its ability to generate diverse and high-quality text.

### Metrics

The primary metric used to evaluate the performance of this model is perplexity. Perplexity measures how well a probability model predicts a sample and is a standard metric for evaluating language models.

## Limitations and Bias

As with any language model, `kz919/open-orca-flan-50k-synthetic-5-models` may have limitations in understanding and generating text on topics outside its training data. Additionally, biases present in the training data may be reflected in the model's outputs. Users should be aware of these limitations and use the model responsibly, especially in sensitive contexts.

## Ethical Considerations

Users are encouraged to consider the ethical implications of using automated language generation in their applications. It is important to ensure that the model's outputs do not perpetuate harmful stereotypes or disseminate misinformation.

## License

This model is released under the Apache-2.0 License.

## Acknowledgments

This model was made possible thanks to the contributions of various models and datasets, including Ignos-Mistral-T5-7B-v1, cognAI-lil-c3po, viethq188-Rabbit-7B-DPO-Chat, cookinai-DonutLM-v1, and v1olet-v1olet-merged-dpo-7B. The training code is based on the `stack_llama_2` script from the Hugging Face TRL repository. 

## More Information

For more details on the model, its usage, and implementation, please visit the model's page on Hugging Face or refer to the [stack_llama_2 script](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py).

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