Welcome to the Mistral LLM Fine-Tuning Project! This repository serves as a resource for the fine-tuning of the Mistral Language Model (LLM) for detecting toxic comment. Whether you're a student, researcher, or enthusiast in the field of artificial intelligence, this project aims to provide a comprehensive guide to fine-tuning Mistral LLM for your specific task.
Mistral LLM is a state-of-the-art language model developed by Mistral AI, based on the GPT (Generative Pre-trained Transformer) architecture. The pretrained model has been trained on a diverse range of text data and possesses a strong understanding of natural language semantics and syntax.
The primary objective of this project is to enable users to fine-tune Mistral LLM for specific downstream tasks, detecting toxic comment. By fine-tuning the model on task-specific data, users can leverage Mistral LLM's pre-trained knowledge to achieve better performance on their target tasks.
To get started with fine-tuning Mistral LLM, follow these steps:
- Clone the Repository: Clone this repository to your local machine using the following command:
git clone https://github.com/your-username/mistral-llm-finetuning.git
- Install Dependencies: Ensure that you have the necessary dependencies installed. You may use a virtual environment to manage dependencies. Install dependencies using:
pip install -q -U bitsandbytes
pip install -q -U git+https://github.com/huggingface/transformers.git
pip install -q -U git+https://github.com/huggingface/peft.git
pip install -q -U git+https://github.com/huggingface/accelerate.git
pip install -q -U datasets
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Prepare Data: We used AiresPucrs/toxic-comments
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Fine-Tune Mistral LLM: Utilize the provided scripts and notebooks to fine-tune Mistral LLM on your data. Experiment with different hyperparameters, training strategies, and model architectures to optimize performance for your task by running train_with_data.py.
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Evaluate Performance: Evaluate the fine-tuned model on your task-specific evaluation metrics. Monitor metrics such as accuracy, precision, recall, F1-score, perplexity, etc., depending on the nature of your task by running inference_with_data.py.
- Documentation: Huggingface contains almost all the documentation you need.
- Pre-trained Models: Access pre-trained Mistral LLM checkpoints for transfer learning or as baselines for fine-tuning.
Contributions to this project are welcome! Whether it's bug fixes, feature enhancements, documentation improvements, or new examples, feel free to submit pull requests to help improve the repository.
If you encounter any issues, have questions, or need assistance with fine-tuning Mistral LLM, don't hesitate to reach out to the maintainers or open an issue on GitHub. We're here to help!
This project is licensed under the Apache-2.0 License.