TinyLlama-1.1B-Chat LoRA Fine-Tuned Model
Table of Contents
Overview
This repository contains a LoRA (Low-Rank Adaptation) fine-tuned version of the TinyLlama/TinyLlama-1.1B-Chat-v0.6 model, optimized for instruction-following and question-answering tasks. The model has been adapted using Parameter-Efficient Fine-Tuning (PEFT) techniques to specialize in conversational AI applications while maintaining the base model's general capabilities.
Model Architecture
- Base Model: TinyLlama-1.1B-Chat (Transformer-based)
- Layers: 22
- Attention Heads: 32
- Hidden Size: 2048
- Context Length: 2048 tokens (limited to 256 during fine-tuning)
- Vocab Size: 32,000
Key Features
- 🚀 Parameter-Efficient Fine-Tuning: Only 0.39% of parameters (4.2M) trained
- 💾 Memory Optimization: 8-bit quantization via BitsAndBytes
- ⚡ Fast Inference: Optimized for conversational response times
- 🤖 Instruction-Tuned: Specialized for Q&A and instructional tasks
- 🔧 Modular Design: Easy to adapt for different use cases
- 📦 Hugging Face Integration: Fully compatible with Transformers ecosystem
Installation
Prerequisites
- Python 3.8+
- PyTorch 2.0+ (with CUDA 11.7+ if GPU acceleration desired)
- NVIDIA GPU (recommended for training and inference)
Package Installation
pip install torch transformers peft accelerate bitsandbytes pandas datasets
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