121 lines
3.8 KiB
Markdown
121 lines
3.8 KiB
Markdown
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---
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language:
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- en
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- zh
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- ja
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- fr
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- de
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- es
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license: apache-2.0
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base_model: google/functiongemma-270m-it
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tags:
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- robotics
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- function-calling
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- gemma
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- lora
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- fine-tuned
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- edge-ai
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- jetson
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- multilingual
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pipeline_tag: text-generation
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library_name: transformers
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---
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# FunctionGemma Robot Actions (Multilingual)
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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.
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## Supported Languages
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🇬🇧 English · 🇨🇳 中文 · 🇯🇵 日本語 · 🇫🇷 Français · 🇩🇪 Deutsch · 🇪🇸 Español
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## Example
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```
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Input: "Can you shake hands with me?" → robot_action(shake_hand) + show_emotion(happy)
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Input: "跟我握手" → robot_action(shake_hand) + show_emotion(happy)
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Input: "握手してください" → robot_action(shake_hand) + show_emotion(happy)
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Input: "Serrez-moi la main" → robot_action(shake_hand) + show_emotion(happy)
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Input: "Gib mir die Hand" → robot_action(shake_hand) + show_emotion(happy)
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Input: "Dame la mano" → robot_action(shake_hand) + show_emotion(happy)
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Input: "我今天心情不好" → robot_action(stand_still) + show_emotion(sad)
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Input: "あれは何ですか？" → robot_action(stand_still) + show_emotion(confused)
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Input: "Raconte-moi une blague" → robot_action(stand_still) + show_emotion(think)
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```
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## Supported Actions
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| Action | Description |
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|--------|-------------|
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| `shake_hand` | Handshake gesture |
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| `face_wave` | Wave hello / goodbye |
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| `hands_up` | Raise both hands |
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| `stand_still` | Stay idle (default for general conversation) |
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| `show_hand` | Show open hand / present card for payment |
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| `do_payment` | Do the payment / do the payment |
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| `down_payment` | Finished the payment |
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## Supported Emotions
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| Emotion | Animation |
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|---------|-----------|
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| `happy` | Happy.riv |
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| `sad` | Sad.riv |
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| `excited` | Excited.riv |
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| `confused` | Confused.riv |
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| `curious` | Curious.riv |
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| `think` | Think.riv |
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Constrained decoding uses 2 forward passes instead of 33 autoregressive steps, achieving ~18x speedup over standard `model.generate()`.
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## Training Details
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| Parameter | Value |
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|-----------|-------|
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| Base model | `google/functiongemma-270m-it` |
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| Method | LoRA (rank 8, alpha 16) |
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| Training data | ~6,000 examples (545 English + ~5,450 multilingual) |
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| Languages | English, Chinese, Japanese, French, German, Spanish |
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| Epochs | 3 |
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| Learning rate | 2e-4 |
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| Batch size | 4 (effective 16 with gradient accumulation) |
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| Max sequence length | 512 |
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| Precision | bf16 |
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| Hardware | NVIDIA RTX 5070 Ti (16 GB) |
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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.
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## Usage
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### Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"OpenmindAGI/functiongemma-finetuned-g1-multilingual",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("OpenmindAGI/functiongemma-finetuned-g1-multilingual")
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model.eval()
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```
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## Citation
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```bibtex
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@misc{openmindagi-functiongemma-multilingual,
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title={FunctionGemma Robot Actions (Multilingual)},
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author={OpenmindAGI},
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year={2025},
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url={https://huggingface.co/OpenmindAGI/functiongemma-finetuned-g1-multilingual}
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}
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```
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## License
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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).
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