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| title: Robot Task Planning - Llama 3.1 8B | |
| emoji: 🤖 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| license: llama3.1 | |
| # 🤖 Robot Task Planning - Llama 3.1 8B (ZeroGPU) | |
| This Space demonstrates a fine-tuned version of Meta's **Llama 3.1 8B** model specialized for **robot task planning** using QLoRA (4-bit quantization + LoRA) technique. | |
| ## 🚀 Hardware: ZeroGPU | |
| This Space uses **ZeroGPU** - dynamic GPU allocation with Nvidia H200: | |
| - **Free** for HuggingFace users | |
| - **Dynamic allocation** - GPU resources allocated on-demand | |
| - **High performance** - H200 offers superior performance | |
| - **60-second duration** per request | |
| ## 🎯 Purpose | |
| Convert natural language commands into structured task sequences for construction robots including: | |
| - **Excavators** - Digging, loading, positioning | |
| - **Dump Trucks** - Material transport, loading, unloading | |
| - **Multi-robot Coordination** - Complex task dependencies | |
| ## 🔗 Model | |
| **Fine-tuned Model**: [YongdongWang/llama-3.1-8b-dart-qlora](https://huggingface.co/YongdongWang/llama-3.1-8b-dart-qlora) | |
| **Base Model**: [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | |
| ## ✨ Features | |
| - 🎮 **Interactive Chat Interface** - Real-time robot command processing | |
| - ⚙️ **Configurable Generation** - Adjust temperature, top-p, max tokens | |
| - 📝 **Example Commands** - Pre-built scenarios to get started | |
| - 🚀 **Optimized Performance** - 4-bit quantization for efficient inference | |
| - 📊 **Structured Output** - JSON-formatted task sequences | |
| - ⚡ **ZeroGPU Powered** - Dynamic GPU allocation for free users | |
| ## 🚀 Usage | |
| 1. **Input**: Natural language robot commands | |
| ``` | |
| "Deploy Excavator 1 to Soil Area 1 for excavation" | |
| ``` | |
| 2. **Output**: Structured task sequences | |
| ```json | |
| { | |
| "tasks": [ | |
| { | |
| "robot": "Excavator_1", | |
| "action": "move_to", | |
| "target": "Soil_Area_1", | |
| "duration": 30 | |
| }, | |
| { | |
| "robot": "Excavator_1", | |
| "action": "excavate", | |
| "target": "Soil_Area_1", | |
| "duration": 120 | |
| } | |
| ] | |
| } | |
| ``` | |
| ## 🛠️ Technical Details | |
| - **Architecture**: Llama 3.1 8B + QLoRA adapters | |
| - **Quantization**: 4-bit (NF4) with double quantization | |
| - **Framework**: Transformers + PEFT + BitsAndBytesConfig | |
| - **Hardware**: ZeroGPU (Dynamic Nvidia H200) | |
| ## ⚡ Performance Notes | |
| - **First Generation**: 5-10 seconds (GPU allocation + model loading) | |
| - **Subsequent Generations**: 2-5 seconds per response | |
| - **Memory Usage**: ~8GB VRAM with 4-bit quantization | |
| - **Context Length**: Up to 2048 tokens | |
| - **GPU Duration**: 60 seconds per request | |
| ## 📚 Example Commands | |
| Try these robot commands: | |
| - `"Deploy Excavator 1 to Soil Area 1 for excavation"` | |
| - `"Send Dump Truck 1 to collect material, then unload at storage"` | |
| - `"Coordinate multiple excavators across different areas"` | |
| - `"Create evacuation sequence for all robots from dangerous zone"` | |
| ## 🔬 Research Applications | |
| This model demonstrates: | |
| - **Natural Language → Robot Planning** translation | |
| - **Multi-agent Task Coordination** | |
| - **Efficient LLM Fine-tuning** with QLoRA | |
| - **Real-time Robot Command Processing** | |
| - **ZeroGPU Integration** for scalable deployment | |
| ## 📄 License | |
| This project uses Meta's Llama 3.1 license. Please review the license terms before use. | |
| ## 🤝 Contributing | |
| For issues, improvements, or questions about the model, please visit the [model repository](https://huggingface.co/YongdongWang/llama-3.1-8b-dart-qlora). | |