Pirate-Language LLM ⚓️
Quantization & Fine-Tuning TinyLlama on a 50k+ Instruction Dataset
📖 Project Overview
This project demonstrates fine-tuning and quantization of the TinyLlama model on a custom dataset of 50k+ instruction-response pairs. The objective was to train a model capable of converting standard English queries into pirate-style responses.
The project highlights how resource-constrained environments (like 4GB GPUs) can still be used effectively to fine-tune large language models using parameter-efficient fine-tuning (LoRA) and quantization (BitsAndBytes).
✨ Key Features
Dataset: 50k+ instruction-response pairs formatted into JSONL for supervised fine-tuning.
Model: TinyLlama fine-tuned with LoRA adapters.
Quantization: 4-bit quantization using BitsAndBytes for reduced memory usage.
Training:
Locally on NVIDIA GTX 1650 (4GB VRAM).
Scaled up to Google Colab T4 (16GB VRAM) for large dataset training.
Optimizations:
Gradient checkpointing
Cosine learning rate sch…
Pirate-Language LLM ⚓️
Quantization & Fine-Tuning TinyLlama on a 50k+ Instruction Dataset
📖 Project Overview
This project demonstrates fine-tuning and quantization of the TinyLlama model on a custom dataset of 50k+ instruction-response pairs. The objective was to train a model capable of converting standard English queries into pirate-style responses.
The project highlights how resource-constrained environments (like 4GB GPUs) can still be used effectively to fine-tune large language models using parameter-efficient fine-tuning (LoRA) and quantization (BitsAndBytes).
✨ Key Features
Dataset: 50k+ instruction-response pairs formatted into JSONL for supervised fine-tuning.
Model: TinyLlama fine-tuned with LoRA adapters.
Quantization: 4-bit quantization using BitsAndBytes for reduced memory usage.
Training:
Locally on NVIDIA GTX 1650 (4GB VRAM).
Scaled up to Google Colab T4 (16GB VRAM) for large dataset training.
Optimizations:
Gradient checkpointing
Cosine learning rate sch…
