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Model: 0x3/functiongemma-finetuned-g1-multilingual Source: Original Platform
2026-07-17 08:48:09 +08:00
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+---
+language:
+  - en
+  - zh
+  - ja
+  - fr
+  - de
+  - es
+license: apache-2.0
+base_model: google/functiongemma-270m-it
+tags:
+  - robotics
+  - function-calling
+  - gemma
+  - lora
+  - fine-tuned
+  - edge-ai
+  - jetson
+  - multilingual
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# FunctionGemma Robot Actions (Multilingual)
+
+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.
+
+## 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](https://huggingface.co/google/functiongemma-270m-it) under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).