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OpenmindAGI/functiongemma-finetuned-g1

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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FunctionGemma Robot Actions

A fine-tuned FunctionGemma 270M 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}
}