Project README

Overview

This repository contains scripts and notebooks for efficiently fine-tuning and interpreting large language models (LLMs) using state-of-the-art quantization, fine-tuning techniques, and interpretability tools. Specifically, the project leverages 4-bit quantization for memory efficiency, LoRA (Low-Rank Adaptation) for fine-tuning efficiency, and LLM Explainer for model interpretability.

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Contents

1. Quantization (model_quantaization.ipynb)

This notebook demonstrates how to load and quantize a full precision FP16 Hugging Face transformer model into 4-bit precision.

Features:

• Efficient loading of large FP16 models.
• Implementation of 4-bit quantization.
• Uploading quantized models to Hugging Face model repository.

Steps included:

• Loading a full FP16 model from Hugging Face.
• Applying quantization techniques (BitsAndBytes).
• Validation and memory footprint analysis.
• Saving and pushing quantized model to Hugging Face Hub.

Requirements:

• Hugging Face transformers
• BitsAndBytes
• PyTorch

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2. Fine-tuning with LoRA (fine_tuning.ipynb)

This notebook provides the procedure to fine-tune the previously quantized model using Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning technique.

Features:

• Efficient fine-tuning with reduced computational requirements.
• Integration with quantized models.

Steps included:

• Loading a quantized model.
• Configuring and applying LoRA.
• Fine-tuning on custom datasets.
• Evaluating model performance during training.

Requirements:

• PEFT (Parameter-Efficient Fine-Tuning) from Hugging Face
• PyTorch
• Transformers

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3. Model Interpretability with LLM Explainer (llm_explainer.ipynb)

This notebook illustrates the use of LLM Explainer for interpreting and analyzing predictions made by fine-tuned LLMs, enhancing transparency and trustworthiness.

Features:

• Explanation of individual model predictions.
• Visualization of attention and important tokens.
• Comparison between predictions.

Steps included:

• Loading fine-tuned LoRA model.
• Using LLM Explainer to generate interpretations.
• Visualization of interpretations.

Requirements:

• LLM Explainer
• Transformers
• PyTorch

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Usage Instructions

Installation

pip install transformers bitsandbytes peft llm_explainer

Running Notebooks

• Ensure each notebook is executed sequentially: first quantize, then fine-tune, and finally interpret the model.
• Adjust configuration parameters in each notebook according to your dataset and model specifics.

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Contributing

Contributions and improvements are welcome! Please submit pull requests or open issues for discussion.

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References

• BitsAndBytes GitHub
• PEFT Hugging Face
• LLM Explainer Documentation

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License

This project is licensed under the MIT License. See the LICENSE file for details.