Instructions to use CuriousDragon/functiongemma-270m-tiny-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CuriousDragon/functiongemma-270m-tiny-agent with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CuriousDragon/functiongemma-270m-tiny-agent")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CuriousDragon/functiongemma-270m-tiny-agent", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CuriousDragon/functiongemma-270m-tiny-agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CuriousDragon/functiongemma-270m-tiny-agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CuriousDragon/functiongemma-270m-tiny-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CuriousDragon/functiongemma-270m-tiny-agent

SGLang

How to use CuriousDragon/functiongemma-270m-tiny-agent with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CuriousDragon/functiongemma-270m-tiny-agent" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CuriousDragon/functiongemma-270m-tiny-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CuriousDragon/functiongemma-270m-tiny-agent" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CuriousDragon/functiongemma-270m-tiny-agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CuriousDragon/functiongemma-270m-tiny-agent with Docker Model Runner:
```
docker model run hf.co/CuriousDragon/functiongemma-270m-tiny-agent
```

functiongemma-270m-tiny-agent / README.md

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	---
	base_model: google/functiongemma-270m-it
	library_name: transformers
	tags:
	- function-calling
	- agents
	- gemma
	- text-generation
	- tiny-agent
	license: gemma
	language:
	- en
	pipeline_tag: text-generation
	---

	# Tiny Agent: FunctionGemma-270m-IT (Fine-Tuned)

	This is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) optimized for reliable function calling.
	It was trained as part of the "Tiny Agent Lab" project to distill the capabilities of larger models into a highly efficient 270M parameter model.

	## Model Description

	- Model Type: Causal LM (Gemma)
	- Language(s): English
	- License: Gemma Terms of Use
	- Finetuned from: google/functiongemma-270m-it

	## Capabilities

	This model is designed to:
	1. Detect User Intent: Accurately identify when a tool call is needed.
	2. Generate Function Calls: Output valid `<start_function_call>` XML/JSON blocks.
	3. Refuse Out-of-Scope Requests: Politely decline requests for which no tool is available.
	4. Ask Clarification: Request missing parameter values interactively.

	## Performance (V4 Evaluation)

	On a held-out test set of 100 diverse queries:
	- Overall Accuracy: 71%
	- Tool Selection Precision: 88%
	- Tool Selection Recall: 94%
	- F1 Score: 0.91

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = "CuriousDragon/functiongemma-270m-tiny-agent"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

	# ... (Add your inference code here)
	```

	## Intended Use

	This model is intended for research and educational purposes in building efficient agentic systems. It works best when provided with a clear system prompt defining the available tools.