gemma4-e4b-openclaw-agent-lora

Gemma 4 E4B OpenClaw Agent LoRA

This repository contains a LoRA adapter fine-tuned from unsloth/gemma-4-E4B-it for an OpenClaw-style Python function-calling coding agent.

Training Summary

• Base model: unsloth/gemma-4-E4B-it
• Dataset: driaforall/pythonic-function-calling (train)
• Training method: Supervised fine-tuning with Unsloth and TRL SFTTrainer
• Adapter: LoRA, r=32, alpha=32, dropout 0
• Tuned modules: language layers, attention modules, and MLP modules; vision layers disabled
• Quantization during training: load_in_4bit=True
• Max sequence length: 2048
• Batching: per-device batch size 1, gradient accumulation 4
• Learning rate: 0.0002
• Training steps: 1000
• Seed: 3407

Data Formatting

The training data was formatted from driaforall/pythonic-function-calling. Function schemas from dataset system messages were preserved and folded into the first user turn to satisfy Gemma's strict user/model chat alternation. Loss was applied only to model response spans with train_on_responses_only.

Intended Use

This adapter is intended for experiments with Pythonic function-calling and coding-agent behavior. It should be evaluated carefully before use in any production agent loop, especially around tool-call validity and safe code execution.