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", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

wenjinf0811 commited on Apr 13

Commit

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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).