Instructions to use OpenmindAGI/functiongemma-finetuned-g1-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenmindAGI/functiongemma-finetuned-g1-multilingual with Transformers:

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

pipe = pipeline("text-generation", model="OpenmindAGI/functiongemma-finetuned-g1-multilingual")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenmindAGI/functiongemma-finetuned-g1-multilingual")
model = AutoModelForCausalLM.from_pretrained("OpenmindAGI/functiongemma-finetuned-g1-multilingual")
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 OpenmindAGI/functiongemma-finetuned-g1-multilingual with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenmindAGI/functiongemma-finetuned-g1-multilingual"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenmindAGI/functiongemma-finetuned-g1-multilingual",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenmindAGI/functiongemma-finetuned-g1-multilingual

SGLang

How to use OpenmindAGI/functiongemma-finetuned-g1-multilingual 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 "OpenmindAGI/functiongemma-finetuned-g1-multilingual" \
    --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": "OpenmindAGI/functiongemma-finetuned-g1-multilingual",
		"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 "OpenmindAGI/functiongemma-finetuned-g1-multilingual" \
        --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": "OpenmindAGI/functiongemma-finetuned-g1-multilingual",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenmindAGI/functiongemma-finetuned-g1-multilingual with Docker Model Runner:
```
docker model run hf.co/OpenmindAGI/functiongemma-finetuned-g1-multilingual
```

functiongemma-finetuned-g1-multilingual / README.md

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	---
	language:
	- en
	- zh
	- ja
	- fr
	- de
	- es
	license: apache-2.0
	base_model: google/functiongemma-270m-it
	tags:
	- robotics
	- function-calling
	- gemma
	- lora
	- fine-tuned
	- edge-ai
	- jetson
	- multilingual
	pipeline_tag: text-generation
	library_name: transformers
	---

	# FunctionGemma Robot Actions (Multilingual)

	A fine-tuned [FunctionGemma 270M](https://huggingface.co/google/functiongemma-270m-it) model that converts natural language into structured robot action and emotion function calls. Supports 6 languages with 98% accuracy at ~59ms on NVIDIA Jetson AGX Thor.

	## Supported Languages

	🇬🇧 English · 🇨🇳 中文 · 🇯🇵 日本語 · 🇫🇷 Français · 🇩🇪 Deutsch · 🇪🇸 Español

	## Example

	```
	Input: "Can you shake hands with me?" → robot_action(shake_hand) + show_emotion(happy)
	Input: "跟我握手" → robot_action(shake_hand) + show_emotion(happy)
	Input: "握手してください" → robot_action(shake_hand) + show_emotion(happy)
	Input: "Serrez-moi la main" → robot_action(shake_hand) + show_emotion(happy)
	Input: "Gib mir die Hand" → robot_action(shake_hand) + show_emotion(happy)
	Input: "Dame la mano" → robot_action(shake_hand) + show_emotion(happy)

	Input: "我今天心情不好" → robot_action(stand_still) + show_emotion(sad)
	Input: "あれは何ですか？" → robot_action(stand_still) + show_emotion(confused)
	Input: "Raconte-moi une blague" → robot_action(stand_still) + show_emotion(think)
	```

	## Supported Actions

	\| Action \| Description \|
	\|--------\|-------------\|
	\| `shake_hand` \| Handshake gesture \|
	\| `face_wave` \| Wave hello / goodbye \|
	\| `hands_up` \| Raise both hands \|
	\| `stand_still` \| Stay idle (default for general conversation) \|
	\| `show_hand` \| Show open hand / present card for payment \|
	\| `do_payment` \| Do the payment / do the payment \|
	\| `down_payment` \| Finished the payment \|

	## Supported Emotions

	\| Emotion \| Animation \|
	\|---------\|-----------\|
	\| `happy` \| Happy.riv \|
	\| `sad` \| Sad.riv \|
	\| `excited` \| Excited.riv \|
	\| `confused` \| Confused.riv \|
	\| `curious` \| Curious.riv \|
	\| `think` \| Think.riv \|


	Constrained decoding uses 2 forward passes instead of 33 autoregressive steps, achieving ~18x speedup over standard `model.generate()`.

	## Training Details

	\| Parameter \| Value \|
	\|-----------\|-------\|
	\| Base model \| `google/functiongemma-270m-it` \|
	\| Method \| LoRA (rank 8, alpha 16) \|
	\| Training data \| ~6,000 examples (545 English + ~5,450 multilingual) \|
	\| Languages \| English, Chinese, Japanese, French, German, Spanish \|
	\| Epochs \| 3 \|
	\| Learning rate \| 2e-4 \|
	\| Batch size \| 4 (effective 16 with gradient accumulation) \|
	\| Max sequence length \| 512 \|
	\| Precision \| bf16 \|
	\| Hardware \| NVIDIA RTX 5070 Ti (16 GB) \|

	Multilingual training data was generated using Claude API — 2 natural phrasings per language per English prompt, resulting in diverse and natural expressions rather than literal translations.

	## Usage

	### Quick Start

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

	model = AutoModelForCausalLM.from_pretrained(
	"OpenmindAGI/functiongemma-finetuned-g1-multilingual",
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)
	tokenizer = AutoTokenizer.from_pretrained("OpenmindAGI/functiongemma-finetuned-g1-multilingual")
	model.eval()
	```

	## Citation

	```bibtex
	@misc{openmindagi-functiongemma-multilingual,
	title={FunctionGemma Robot Actions (Multilingual)},
	author={OpenmindAGI},
	year={2025},
	url={https://huggingface.co/OpenmindAGI/functiongemma-finetuned-g1-multilingual}
	}
	```

	## License

	Fine-tuned from [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).