🚀 LLM Fine-Tuning with Unsloth + Hugging Face

Fine-tune large language models (LLMs) like Mistral or LLaMA efficiently on custom instruction datasets using Unsloth, Hugging Face Transformers, and export to GGUF for fast inference via llama.cpp.

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📌 Project Summary

This project demonstrates how to fine-tune an LLM on a custom instruction-based dataset using:

• 🧠 Unsloth for memory-efficient fine-tuning
• 🔧 TRL’s SFTTrainer from Hugging Face for supervised training
• 💾 GGUF Export for inference-ready deployment (supports llama.cpp, llamafile, etc.)
• 📊 Optional W&B tracking for experiment visualization

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🧱 Tech Stack

Component	Tool/Library
Model	Mistral / LLaMA
Trainer	TRL's SFTTrainer
Optimization	AdamW (8-bit), LR Schedulers
Quantization	8-bit / 4-bit via GGUF
Logging	Weights & Biases (optional)
Hardware Target	Colab / Kaggle GPU

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✅ Features

• 🔧 Fine-tunes Mistral or LLaMA models with minimal VRAM requirements
• 🧠 Supports instruction tuning for domain-specific and structured tasks
• ⚡ Trains with 8-bit optimizer using bitsandbytes for faster and lighter execution
• 📦 Exports the final model in GGUF format compatible with llama.cpp, llamafile, etc.
• 🎯 Runs on Kaggle, Google Colab, or custom local GPU environments
• 📊 Optional Weights & Biases (W&B) logging for real-time experiment tracking

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🧪 Training Configuration

Hyperparameter	Value
Epochs	2–3
Batch Size	2 (with accumulation = 8)
Max Steps	100
Learning Rate	2e-4
Optimizer	AdamW (8-bit)
Precision	fp16 / bf16
Quantization	GGUF export (8-bit)

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