gemma4-31b-ja-agent-coder

gemma4-31b-ja-agent-coder

Japanese-enhanced agentic coding model — Fine-tuned gemma4-31b-it for autonomous coding agents with Japanese language support.

Highlights

• Agentic behavior: ReAct reasoning, multi-step tool calling, self-correction
• Japanese coding: Code generation, review, debugging in Japanese
• Claude Code compatible: Designed as a local subagent for Claude Code via MCP
• Function calling: Native Ollama/OpenAI tool use format
• Zero API cost: Runs locally on 20GB+ VRAM

Benchmark Results

Evaluated on 12 task categories across agentic coding capabilities. Each criterion is scored 0-1, averaged per category (scale 0-10).

Key Improvements

• Function Calling: Clean <tool_call> JSON format output (base model adds extra explanation)
• Multi-step ReAct: Structured JSON reasoning with proper Thought/Action/Observation flow
• Code Review: Parameterized query suggestions for SQL injection fixes
• Database CRUD: Complete Create/Read/Update/Delete coverage

Inference Test Results (v2 adapter)

Training Details

Training Data Categories

Use with Ollama

# After GGUF conversion
ollama create gemma4-ja-agent-coder -f Modelfile
ollama run gemma4-ja-agent-coder

Use with helix-agents (Claude Code MCP)

Reduce Claude Code API token consumption by delegating routine tasks to this local model.

{
  "mcpServers": {
    "helix-agents": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/helix-agent", "python", "server.py"]
    }
  }
}

Use with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                          bnb_4bit_compute_dtype=torch.bfloat16)
base = AutoModelForCausalLM.from_pretrained("google/gemma-4-31b-it",
                                              quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(base, "Tsunamayo7/gemma4-31b-ja-agent-coder")
tokenizer = AutoTokenizer.from_pretrained("Tsunamayo7/gemma4-31b-ja-agent-coder")

Note: Gemma4 uses Gemma4ClippableLinear which requires a PEFT monkey-patch. See this gist for the workaround.

License

Apache 2.0 (same as base model)

Author

tsunamayo7 — Builder of helix-agents, a local LLM delegation framework for Claude Code.