115 lines
2.8 KiB
Markdown
115 lines
2.8 KiB
Markdown
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---
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language:
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- en
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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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pipeline_tag: text-generation
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library_name: transformers
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---
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# FunctionGemma Robot Actions
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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. Designed for real-time inference on edge devices like the NVIDIA Jetson AGX Thor.
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## Overview
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This model takes a user's voice or text input and outputs two function calls:
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- **`robot_action`** — a physical action for the robot to perform
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- **`show_emotion`** — an emotion to display on the robot's avatar screen (Rive animations)
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General conversation defaults to `stand_still` with a contextually appropriate emotion.
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## Example
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```
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Input: "Can you shake hands with me?"
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Output: robot_action(action_name="shake_hand") + show_emotion(emotion="happy")
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Input: "What is that?"
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Output: robot_action(action_name="stand_still") + show_emotion(emotion="confused")
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Input: "I feel sad"
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Output: robot_action(action_name="stand_still") + show_emotion(emotion="sad")
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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 |
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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 |
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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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## Performance on NVIDIA Jetson AGX Thor
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Benchmarked with constrained decoding (2 forward passes instead of 33 autoregressive steps):
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| Metric | Value |
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|--------|-------|
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| Min latency | 52 ms |
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| Max latency | 72 ms |
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| **Avg latency** | **59 ms** |
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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 | 545 examples (490 train / 55 eval) |
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| Epochs | 5 |
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| Learning rate | 2e-4 |
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| Batch size | 2 (effective 4 with gradient accumulation) |
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| Max sequence length | 512 |
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| Precision | bf16 |
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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-robot-actions",
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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-robot-actions")
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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-robot-actions,
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title={FunctionGemma Robot Actions},
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author={OpenmindAGI},
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year={2025},
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url={https://huggingface.co/OpenmindAGI/functiongemma-robot-actions}
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}
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```
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