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

Libraries

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

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

pipe = pipeline("text-generation", model="OpenmindAGI/functiongemma-finetuned-g1")
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")
model = AutoModelForCausalLM.from_pretrained("OpenmindAGI/functiongemma-finetuned-g1")
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 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

wenjinf0811 commited on Apr 3

Commit

7fb20e5

verified ·

1 Parent(s): 11a12f8

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +97 -0

README.md ADDED Viewed

	@@ -0,0 +1,97 @@

+# FunctionGemma Robot Actions
+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. Designed for real-time inference on edge devices like the NVIDIA Jetson AGX Thor.
+## Overview
+This model takes a user's voice or text input and outputs two function calls:
+- **`robot_action`** — a physical action for the robot to perform
+- **`show_emotion`** — an emotion to display on the robot's avatar screen (Rive animations)
+General conversation defaults to `stand_still` with a contextually appropriate emotion.
+## Example
+```
+Input:  "Can you shake hands with me?"
+Output: robot_action(action_name="shake_hand") + show_emotion(emotion="happy")
+Input:  "What is that?"
+Output: robot_action(action_name="stand_still") + show_emotion(emotion="confused")
+Input:  "I feel sad"
+Output: robot_action(action_name="stand_still") + show_emotion(emotion="sad")
+```
+## Supported Actions
+| Action | Description |
+|--------|-------------|
+| `shake_hand` | Handshake gesture |
+| `face_wave` | Wave hello |
+| `hands_up` | Raise both hands |
+| `stand_still` | Stay idle (default for general conversation) |
+| `show_hand` | Show open hand |
+## Supported Emotions
+| Emotion | Animation |
+|---------|-----------|
+| `happy` | Happy.riv |
+| `sad` | Sad.riv |
+| `excited` | Excited.riv |
+| `confused` | Confused.riv |
+| `curious` | Curious.riv |
+| `think` | Think.riv |
+## Performance on NVIDIA Jetson AGX Thor
+Benchmarked with constrained decoding (2 forward passes instead of 33 autoregressive steps):
+| Metric | Value |
+|--------|-------|
+| Min latency | 52 ms |
+| Max latency | 72 ms |
+| **Avg latency** | **59 ms** |
+## Training Details
+| Parameter | Value |
+|-----------|-------|
+| Base model | `google/functiongemma-270m-it` |
+| Method | LoRA (rank 8, alpha 16) |
+| Training data | 545 examples (490 train / 55 eval) |
+| Epochs | 5 |
+| Learning rate | 2e-4 |
+| Batch size | 2 (effective 4 with gradient accumulation) |
+| Max sequence length | 512 |
+| Precision | bf16 |
+### Quick Start
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    "OpenmindAGI/functiongemma-robot-actions",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("OpenmindAGI/functiongemma-robot-actions")
+model.eval()
+```
+## Citation
+```bibtex
+@misc{openmindagi-functiongemma-robot-actions,
+  title={FunctionGemma Robot Actions},
+  author={OpenmindAGI},
+  year={2025},
+  url={https://huggingface.co/OpenmindAGI/functiongemma-robot-actions}
+}
+```