Gemma 3-1B Fine-Tuning Project
An efficient, local code assistant fine-tuned on the Alpaca code instructions dataset. This project demonstrates the complete lifecycle of fine-tuning a Large Language Model (LLM) for specific coding tasks using Unsloth and LoRA, transforming the lightweight Gemma 3-1B model into a capable Python coding assistant.
🚀 Key Features
- End-to-end fine-tuning pipeline for LLMs
- Efficient data subset creation for rapid prototyping
- LoRA-based parameter-efficient training
- Model merging and GGUF export for local inference
- Interactive CLI-based inference interface
- Fully documented, reproducible workflow
🛠️ Tech Stack
| Layer | Technology |
|---|---|
| Model | Gemma 3-1B (Unsloth) |
| Training | PyTorch, LoRA, TRL, PEFT |
| Data | Alpaca 120k Code Instructions |
| Export | GGUF, bitsandbytes |
| Inference | CLI, 4-bit quantization |
📊 Key Metrics / Status
- Model size: 1B parameters
- Training dataset: 120,000 code instructions
- Hardware: Consumer GPU (NVIDIA T4/RTX)
- Status: Complete, ready for local inference
📚 Documentation Map
| Document | Description |
|---|---|
| SPEC_OVERVIEW | Technical architecture & spec |
| IMPLEMENTATION_OVERVIEW | Build plan & phases |
| REFERENCES | External links & resources |
| GEMMA3-1B_ARCHITECTURE | System diagrams |
| DIRECTORY_STRUCTURE | Full navigation sitemap |
👤 Reading Paths
- New to the project? Start with SPEC_OVERVIEW
- Ready to build? Go to IMPLEMENTATION_OVERVIEW
- Looking something up? See QUICK_REFERENCE
Last Updated: 2026-04-29 | Status: Complete