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language:
- en
license: apache-2.0
library_name: peft
base_model: unsloth/functiongemma-270m-it
tags:
- function-calling
- tool-use
- gemma3
- lora
- peft
datasets:
- Salesforce/xlam-function-calling-60k
- MadeAgents/xlam-irrelevance-7.5k
pipeline_tag: text-generation
---
# sumitagrawal/functiongemma-270m-tool-agent
Fine-tuned [FunctionGemma 270M](https://huggingface.co/unsloth/functiongemma-270m-it) LoRA adapter
specialized for **general tool/function calling**.
| | Link |
|---|---|
| **Source code** | [tech-sumit/tool-agent](https://github.com/tech-sumit/tool-agent) |
| **Blog post** | [sumitagrawal.dev/blog/finetuning-functiongemma-270m-tool-calling](https://sumitagrawal.dev/blog/finetuning-functiongemma-270m-tool-calling/) |
| **Base model** | [unsloth/functiongemma-270m-it](https://huggingface.co/unsloth/functiongemma-270m-it) |
## Benchmark Results
Evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) on 100 held-out general function-calling examples. End-to-end through the [tool agent](https://github.com/tech-sumit/tool-agent) pipeline: **14% → 57%** tool selection accuracy on a 7-query evaluation.
## Training
- **Base model**: [`unsloth/functiongemma-270m-it`](https://huggingface.co/unsloth/functiongemma-270m-it) (Gemma 3 270M)
- **Method**: [LoRA](https://arxiv.org/abs/2106.09685) (r=16, alpha=32) via [PEFT](https://huggingface.co/docs/peft) + [TRL](https://huggingface.co/docs/trl) SFTTrainer
- **Dataset**: 13,000 general function-calling examples
- **Epochs**: 3
- **Training time**: 25 minutes
- **Hardware**: NVIDIA H100 SXM 80GB via [vast.ai](https://vast.ai)
### Data composition
| Source | Examples | Purpose |
|--------|----------|---------|
| [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | ~10,000 | General function calling |
| [MadeAgents/xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k) | ~3,000 | Negative examples / refusal |
| **Total** | **~13,000** | |
## Usage
### With PEFT
```python
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
```bash
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:
```bash
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).
|