Instructions to use ankit-pn/functiongemma-270m-phone-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ankit-pn/functiongemma-270m-phone-assistant with Transformers:

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

pipe = pipeline("text-generation", model="ankit-pn/functiongemma-270m-phone-assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ankit-pn/functiongemma-270m-phone-assistant")
model = AutoModelForCausalLM.from_pretrained("ankit-pn/functiongemma-270m-phone-assistant", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ankit-pn/functiongemma-270m-phone-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ankit-pn/functiongemma-270m-phone-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ankit-pn/functiongemma-270m-phone-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ankit-pn/functiongemma-270m-phone-assistant

SGLang

How to use ankit-pn/functiongemma-270m-phone-assistant 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 "ankit-pn/functiongemma-270m-phone-assistant" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ankit-pn/functiongemma-270m-phone-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ankit-pn/functiongemma-270m-phone-assistant" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ankit-pn/functiongemma-270m-phone-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ankit-pn/functiongemma-270m-phone-assistant with Docker Model Runner:
```
docker model run hf.co/ankit-pn/functiongemma-270m-phone-assistant
```

FunctionGemma 270M: phone assistant (20 tools, English + Hinglish)

This is a full-parameter fine-tune of google/functiongemma-270m-it. It is a Siri-style assistant that turns a user command into calls to 20 phone tools: alarms, timers, date/time, flashlight, Wi-Fi, Bluetooth, volume, brightness, weather, calendar, reminders, messages, calls, apps, music, media control, navigation, web search and notes. For small talk or unsupported requests it replies without calling a tool.

Training data: ankit-pn/phone-assistant-function-calling (3,200 train / 800 test, 15% Hinglish)
Code, report and raw predictions: https://github.com/ankit-pn/functiongemma-phone-assistant-bench

Results (800 held-out examples, all 20 tools in every prompt, greedy decoding)

Metric	Base	This model
Exact match (all functions + arguments)	22.3%	75.4%
Tool selection	31.6%	93.0%
Parse errors	10.6%	0.25%
No-call recall (stays silent when it should)	90.4%	79.8%

Exact match by slice: single call 75.3%, parallel (2–3 calls) 70.8%, English 79.3%, Hinglish 53.3%. The confidence interval on the exact-match gain is [+49.2, +56.9] pp (95%, cluster bootstrap). Known weaknesses: it over-calls on Hinglish small talk, garbles some names, misconverts some times, and phrases free-text arguments differently from the gold labels.

Usage

Use the same tool schemas and developer prompt that the model was trained with. They are in tools.json and prompting.py in the GitHub repo. The model emits FunctionGemma's call syntax.

import json
from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "ankit-pn/functiongemma-270m-phone-assistant"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)
tools = json.load(open("tools.json"))          # from the GitHub repo
from prompting import SYSTEM                  # from the GitHub repo

msgs = [{"role": "developer", "content": SYSTEM},
        {"role": "user", "content": "kal subah 6:30 ka alarm laga do aur wifi band kar do"}]
ids = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True, return_tensors="pt", return_dict=True)
out = model.generate(**ids, max_new_tokens=128, do_sample=False)
print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=False))
# <start_function_call>call:set_alarm{time:<escape>06:30<escape>}<end_function_call><start_function_call>call:toggle_wifi{state:<escape>off<escape>}<end_function_call>...

score.py in the GitHub repo has a strict parser for this output format.

Training

3,200 examples, 3 epochs, 600 steps, effective batch 16, LR 5e-5 with cosine schedule and 5% warmup. Loss on assistant tokens only. Two Kaggle T4s, FP32 weights with BF16 autocast, 85 minutes.
The weights are saved in FP32. The final checkpoint is used as-is; it was not selected on test data.
Note: the chat template of the Kaggle copy of FunctionGemma v1 duplicates }<end_function_call> in tool-call turns. This model was trained with the Hugging Face hub template, which is included here.

Limitations

Results come from a single seed. The dataset was written by LLMs following a spec, not collected from real users. This is a research artifact: validate the calls before executing them on a real device. Use is subject to the Gemma Terms of Use.

Downloads last month: 417

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for ankit-pn/functiongemma-270m-phone-assistant

Base model

google/functiongemma-270m-it

Finetuned

(463)

this model

ankit-pn
/

functiongemma-270m-phone-assistant