Fine-tuning large language models like LLaMA has transformed the way we adapt pre-trained models for specialized tasks. This repository focuses on parameter-efficient fine-tuning techniques such as LoRA and QLoRA to adapt the LLaMA2-7B model to Indian legal text datasets.
You are tasked with fine-tuning the LLaMA2-7B model on a dataset related to Indian laws to make it capable of generating context-aware legal insights. The challenge is to leverage advanced fine-tuning techniques like LoRA/QLoRA to optimize the training process while keeping computational requirements minimal. Demonstrate your skills in model tuning and deployment!
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Refer to articles, research papers, and official documentation for guidance on techniques and best practices.
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Do not alter any pre-written code or comments.
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Write code only in the provided space and document your steps with comments for better understanding.
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Use Google Colab or similar GPU-enabled environments for training and testing the model.
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Help
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For any queries or support, feel free to reach out via email at [email protected] or [email protected] or join the discussion on the project’s Discord server.
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Contributions are welcome! Follow these steps:
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Fork this repository and clone it to your local device.
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Work on individual tasks in a separate branch.
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Push your updates to the forked repo and create a Pull Request (PR).
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Your PR will be reviewed, and upon approval, merged into the main repository.
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Dataset: Indian Law Dataset (https://huggingface.co/datasets/jizzu/llama2_indian_law_v2)
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Parameter-Efficient Fine-Tuning: LoRA Paper (https://arxiv.org/pdf/1902.00751)
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Hugging Face Transformers Documentation: Link(https://huggingface.co/docs/transformers/index)