IndianLegalLlama-1B

Llama 3.2 1B Fine-Tuning (Instruction SFT)

Overview

This project demonstrates fine-tuning of Meta Llama 3.2 1B using supervised fine-tuning (SFT) for instruction-following tasks. The objective was to adapt the base model to produce domain-relevant responses while maintaining the general reasoning ability of the original Llama architecture.

The training was performed using parameter-efficient fine-tuning (PEFT) with LoRA adapters to reduce compute cost and memory consumption while achieving strong performance.

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Base Model

• Model: Meta Llama 3.2 1B
• Architecture: Transformer-based causal language model
• Training Method: Supervised Fine-Tuning (SFT)
• Fine-Tuning Technique: LoRA (Low-Rank Adaptation)

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Hardware Setup

Training was executed on:

• GPU: NVIDIA RTX 4090 (24GB VRAM)
• CUDA Support: Enabled
• Precision: Mixed precision (FP16 / QLoRA supported)
• Training Time: ~110 minutes (approximate, varies by configuration)

The RTX 4090 provides excellent throughput for parameter-efficient tuning, enabling large model experimentation on consumer-grade hardware.

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Dataset

The training dataset consists of instruction-response pairs structured as:

{ "instruction": "...", "response": "..." }