Instructions to use essobi/functiongemma-mobile-actions-v6-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use essobi/functiongemma-mobile-actions-v6-16bit with Transformers:

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

pipe = pipeline("text-generation", model="essobi/functiongemma-mobile-actions-v6-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("essobi/functiongemma-mobile-actions-v6-16bit")
model = AutoModelForCausalLM.from_pretrained("essobi/functiongemma-mobile-actions-v6-16bit")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use essobi/functiongemma-mobile-actions-v6-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "essobi/functiongemma-mobile-actions-v6-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "essobi/functiongemma-mobile-actions-v6-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/essobi/functiongemma-mobile-actions-v6-16bit

SGLang

How to use essobi/functiongemma-mobile-actions-v6-16bit 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 "essobi/functiongemma-mobile-actions-v6-16bit" \
    --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": "essobi/functiongemma-mobile-actions-v6-16bit",
		"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 "essobi/functiongemma-mobile-actions-v6-16bit" \
        --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": "essobi/functiongemma-mobile-actions-v6-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use essobi/functiongemma-mobile-actions-v6-16bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for essobi/functiongemma-mobile-actions-v6-16bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for essobi/functiongemma-mobile-actions-v6-16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for essobi/functiongemma-mobile-actions-v6-16bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="essobi/functiongemma-mobile-actions-v6-16bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use essobi/functiongemma-mobile-actions-v6-16bit with Docker Model Runner:
```
docker model run hf.co/essobi/functiongemma-mobile-actions-v6-16bit
```

essobi commited on Jan 27

Commit

c5a60f7

verified ·

1 Parent(s): 44218e0

Upload README.md with huggingface_hub

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README.md +57 -13

README.md CHANGED Viewed

@@ -1,21 +1,65 @@
 ---
-base_model: unsloth/functiongemma-270m-it
 tags:
-- text-generation-inference
-- transformers
-- unsloth
-- gemma3_text
-license: apache-2.0
 language:
-- en
 ---
-# Uploaded finetuned  model
-- **Developed by:** essobi
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/functiongemma-270m-it
-This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+license: gemma
+library_name: transformers
 tags:
+  - function-calling
+  - tool-use
+  - mobile
+  - gemma
+  - unsloth
+  - fine-tuned
+base_model: google/gemma-3-1b-it
+datasets:
+  - google/mobile-actions
+pipeline_tag: text-generation
 language:
+  - en
 ---
+# FunctionGemma Mobile Actions v6
+A fine-tuned version of [FunctionGemma 270M](https://huggingface.co/google/gemma-3-1b-it) optimized for mobile device function calling. This model excels at understanding natural language commands and mapping them to structured function calls for common mobile actions.
+## Model Description
+- **Base Model:** google/gemma-3-1b-it (270M parameters)
+- **Fine-tuning Method:** LoRA (r=128, alpha=128)
+- **Training Data:** [google/mobile-actions](https://huggingface.co/datasets/google/mobile-actions) + synthetic augmentation
+- **Optimized For:** Mobile assistant function calling
+## Supported Functions
+| Function | Description | Example Input |
+|----------|-------------|---------------|
+| `set_alarm` | Set alarms | "Wake me up at 7am" |
+| `create_reminder` | Create reminders | "Remind me to buy milk" |
+| `set_timer` | Set countdown timers | "Timer for 10 minutes" |
+| `make_call` | Make phone calls | "Call Mom" |
+| `send_message` | Send text messages | "Text John I'm running late" |
+| `create_calendar_event` | Schedule events | "Schedule meeting at 3pm" |
+| `play_music` | Play music | "Play some jazz" |
+| `get_weather` | Get weather info | "What's the weather like?" |
+| `open_app` | Open applications | "Open the camera" |
+| `navigate` | Get directions | "Navigate to the airport" |
+| `set_volume` | Adjust volume | "Turn the volume up" |
+| `calculator` | Math calculations | "What's 15 times 23?" |
+## Usage
+```python
+from vllm import LLM, SamplingParams
+llm = LLM(model="essobi/functiongemma-mobile-actions-v6-16bit", trust_remote_code=True)
+# See full documentation for prompt format and tool definitions
+```
+## Output Format
+```
+<start_function_call>call:function_name{param1:<escape>value1<escape>,param2:<escape>value2<escape>}<end_function_call>
+```
+## License
+This model is released under the [Gemma License](https://ai.google.dev/gemma/terms).