sumitagrawal/functiongemma-270m-tool-agent

Fine-tuned FunctionGemma 270M LoRA adapter specialized for general tool/function calling.

	Link
Source code	tech-sumit/tool-agent
Blog post	sumitagrawal.dev/blog/finetuning-functiongemma-270m-tool-calling
Base model	unsloth/functiongemma-270m-it

Benchmark Results

Evaluated using lm-evaluation-harness on 100 held-out general function-calling examples. End-to-end through the tool agent pipeline: 14% → 57% tool selection accuracy on a 7-query evaluation.

Training

• Base model: unsloth/functiongemma-270m-it (Gemma 3 270M)
• Method: LoRA (r=16, alpha=32) via PEFT + TRL SFTTrainer
• Dataset: 13,000 general function-calling examples
• Epochs: 3
• Training time: 25 minutes
• Hardware: NVIDIA H100 SXM 80GB via vast.ai

Data composition

Source	Examples	Purpose
Salesforce/xlam-function-calling-60k	~10,000	General function calling
MadeAgents/xlam-irrelevance-7.5k	~3,000	Negative examples / refusal
Total	~13,000

Usage

With PEFT

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("unsloth/functiongemma-270m-it", torch_dtype="auto")
model = PeftModel.from_pretrained(base, "sumitagrawal/functiongemma-270m-tool-agent")
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained("sumitagrawal/functiongemma-270m-tool-agent")

prompt = """<start_of_turn>user
You are a model that can do function calling with the following functions

{"name": "get_weather", "description": "Get current weather", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}}
{"name": "send_email", "description": "Send an email", "parameters": {"type": "object", "properties": {"to": {"type": "string"}, "subject": {"type": "string"}, "body": {"type": "string"}}, "required": ["to", "subject", "body"]}}

What's the weather in Tokyo?<end_of_turn>
<start_of_turn>model
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128, temperature=0.1, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# <start_function_call>call:get_weather{city:<escape>Tokyo<escape>}<end_function_call>

With the Tool Agent Server

git clone https://github.com/tech-sumit/tool-agent.git
cd tool-agent
pip install -e .

TOOL_AGENT_BACKEND=transformers \
TOOL_AGENT_MODEL=./models/finetuned \
python -m agent.server
# Server starts on http://localhost:8888 with REST, WebSocket, MCP, and A2A

With Ollama (GGUF)

Export to GGUF first, then:

ollama create tool-agent -f Modelfile
ollama run tool-agent

Output format

The model uses FunctionGemma's native control-token format:

<start_function_call>call:function_name{param1:<escape>value1<escape>,param2:<escape>value2<escape>}<end_function_call>

License

Apache 2.0 (same as the base model).