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

File size: 3,928 Bytes

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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).