Gemma 3-1B — Technical Specification
1. Overview
This document details the technical architecture and design of the Gemma 3-1B fine-tuning project. The project transforms the base Gemma 3-1B model into a specialized Python code assistant using Unsloth and LoRA techniques.
2. System Architecture
- Base Model: unsloth/gemma-3-1b-it (instruction-tuned)
- Training Data: Alpaca 120k code instructions
- Training Framework: PyTorch, TRL, PEFT, Unsloth
- Parameter-Efficient Fine-Tuning: LoRA adapters
- Quantization: 4-bit for memory efficiency
3. Component Breakdown
- Data Preparation: Subset creation for rapid prototyping
- Fine-Tuning Engine: Orchestrates model training with LoRA
- Model Merging: Fuses LoRA adapters into the base model
- Export: Converts to GGUF format for local inference
- Inference: CLI chat interface for model evaluation
4. Data Flow / API Design
- Data flows from raw dataset → subset → training → checkpoint → merged model → GGUF export → inference interface.
5. Technology Decisions & Rationale
- Unsloth: Chosen for speed and memory efficiency
- LoRA: Enables parameter-efficient training
- GGUF: Ensures compatibility with local inference tools
6. Constraints & Assumptions
- Requires consumer GPU with sufficient VRAM
- Assumes access to Alpaca dataset and Hugging Face models
7. Open Questions / Future Scope
- Explore larger model variants
- Integrate web-based inference UI
- Experiment with additional datasets
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Last Updated: 2026-04-29
Version: 1.0
Status: Complete