Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,97 @@
+# FunctionGemma Robot Actions
+
+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.
+
+## 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
+
+```python
+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
+
+```bibtex
+@misc{openmindagi-functiongemma-robot-actions,
+  title={FunctionGemma Robot Actions},
+  author={OpenmindAGI},
+  year={2025},
+  url={https://huggingface.co/OpenmindAGI/functiongemma-robot-actions}
+}
+```
+