functiongemma-finetuned-g1/README.md

language, license, base_model, tags, pipeline_tag, library_name

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

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

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

@misc{openmindagi-functiongemma-robot-actions,
  title={FunctionGemma Robot Actions},
  author={OpenmindAGI},
  year={2025},
  url={https://huggingface.co/OpenmindAGI/functiongemma-robot-actions}
}