Gemma 3-1B — Implementation Guide
This document details the practical steps, scripts, and code used to build, train, and deploy the Gemma 3-1B fine-tuned model.
1. Data Preparation: Subset Creation
Efficiently process large datasets by creating manageable subsets for rapid prototyping and debugging.
Script: create_subset.py
- Reads the massive Alpaca 120k dataset.
- Randomly samples a specified number of items (default: 1,000).
- Outputs a new, valid JSON file for use in training.
Example Code
import json
import random
def create_subset(input_file, output_file, subset_size=1000):
# ...existing code from create_subset.md...2. Fine-Tuning Engine
The core training process, leveraging Unsloth and LoRA for efficient parameter tuning.
Script: main.py
- Loads the base model in 4-bit quantization.
- Attaches LoRA adapters for parameter-efficient training.
- Uses SFTTrainer for supervised fine-tuning.
- Generates training reports and visualizations.
Example Code
# ...key code from main.md (see full technical spec for details)...3. Model Merging
After training, merge LoRA adapters into the base model to create a standalone, deployable model.
Script: merge_lora.py
- Loads the checkpoint with LoRA adapters.
- Merges adapters into the base model (16-bit precision).
- Saves the unified model to the
merged_modeldirectory.
Example Code
# ...key code from merge_lora.md...4. Model Export: GGUF Conversion
Convert the merged model to GGUF format for compatibility with local inference tools.
Script: export_gguf.py
- Loads the merged model.
- Converts and quantizes to GGUF (q8_0).
- Outputs
.gguffile for use with llama.cpp, LM Studio, etc.
Example Code
# ...key code from export_gguf.md...5. Inference Interface
Interactive CLI chat interface for model evaluation and demonstration.
Script: inference.py
- Loads the final model from
merged_model. - Formats prompts in Alpaca style.
- Maintains conversation history for multi-turn interactions.
Example Code
# ...key code from inference.md...🔗 Related Documents
Last Updated: 2026-04-29
Version: 1.0
Status: Complete