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0x3/functiongemma-finetuned-g1-multilingual

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Model Card

FunctionGemma Robot Actions (Multilingual)

A fine-tuned FunctionGemma 270M 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 under Apache 2.0.