Fine-tuning-LLMs

fine-tune of different type LLMs with the various dataset (Available+Own) used for learning purpose.

This repository demonstrates fine-tuning techniques for low-parameter Large Language Models (LLMs) using several advanced methods:

• Parameter-Efficient Fine-Tuning (PEFT) with LoRA and QLoRA
• Reinforcement Learning from Human Feedback (RLHF)
• Reinforcement Learning from AI Feedback (RLAIF)

Overview

The repository showcases practical examples and scripts for efficiently fine-tuning LLMs (1B–13B parameters). We cover:

• LoRA and QLoRA for low-resource environments.
• RLHF to align models with human preferences.
• RLAIF as a scalable alternative using AI-generated preference labels.

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Features

• Modular and reproducible fine-tuning pipelines.
• Support for Hugging Face Transformers and PEFT libraries.
• RLHF implementation using reward models and PPO training loops.
• RLAIF framework using synthetic preference datasets.

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✨ Acknowledgements

• Hugging Face Transformers
• PEFT Library
• TRL Library

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📌 Notes

• For large model training, ensure you have access to GPUs with sufficient VRAM.
• For QLoRA, bitsandbytes and quantization setup is required.
• RLHF and RLAIF implementations require careful tuning of reward models and PPO hyperparameters.

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