Spaces:
Runtime error
Runtime error
File size: 3,142 Bytes
1f65ffa 5f15f77 1f65ffa 8e21b19 1f65ffa 5f15f77 0af933c 1f65ffa 0af933c 5f15f77 98c5195 5f15f77 98c5195 5f15f77 98c5195 5f15f77 98c5195 5f15f77 98c5195 8e21b19 c3abc84 98c5195 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
---
title: Robot Task Planning - Llama 3.1 8B
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.32.2
app_file: app.py
pinned: false
license: llama3.1
hardware: t4-medium
---
# 🤖 Robot Task Planning - Llama 3.1 8B
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.
## 🎯 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
## 🚀 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
- **Interface**: Gradio 4.32.2 (stable version)
- **Hardware**: T4-MEDIUM (16GB VRAM)
## ⚡ Performance Notes
- **First Load**: 3-5 minutes (model downloading + loading)
- **Subsequent Generations**: ~2-10 seconds per response
- **Memory Usage**: ~8GB VRAM with 4-bit quantization
- **Context Length**: Up to 2048 tokens
## 📚 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**
## 📄 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).
|