---
title: VibeThinker-1.5B Competitive Coding Assistant
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
---

# 🧠 VibeThinker-1.5B Competitive Coding Assistant

An interactive demo of **VibeThinker-1.5B** optimized for competitive programming challenges.

## ⚡ Performance Highlights

- **AIME24**: 80.3 (surpasses DeepSeek R1's 79.8)
- **AIME25**: 74.4 (vs DeepSeek R1's 70.0)  
- **LiveCodeBench V6**: 51.1 (competitive coding)
- **Training Cost**: Only $7,800 USD
- **Parameters**: 1.5B (400× smaller than DeepSeek R1)

## 🎯 What It's Best At

✅ **Competitive Programming**: LeetCode, Codeforces, AtCoder-style algorithm problems  
✅ **Python Coding Challenges**: Problems with clear input/output specifications  
✅ **Mathematical Reasoning**: Complex proofs and formal reasoning tasks  
✅ **Algorithm Design**: Dynamic programming, graph algorithms, optimization problems

## ⚠️ Important Limitations

This model is **specialized for competitive programming**, not general software development:

❌ Not suitable for: Building applications, debugging real codebases, using specific libraries  
❌ Limited knowledge: Low encyclopedic knowledge, Python-focused training  
❌ Overthinking tendency: May generate verbose reasoning for simple tasks  
❌ Narrow scope: Optimized for benchmark-style problems, not production code

*See [community feedback analysis](https://www.reddit.com/r/LocalLLaMA/comments/1ou1emx/) for detailed real-world testing insights*

## 🚀 Features

- **🧠 Intelligent Parsing**: Automatic separation of reasoning and solution
- **📊 Token Tracking**: Real-time stats on generation time and token usage
- **💻 Clean Code Display**: Syntax-highlighted, copyable/downloadable code blocks
- **📱 Responsive Design**: Modern UI with collapsible reasoning sections
- **🎨 High Contrast**: Readable output with dark code blocks on white background
- **🔄 Loop Detection**: Automatically detects and truncates repetitive output

## 🛠️ Technical Details

### Model Information
- **Base Model**: Qwen2.5-Math-1.5B
- **Training Method**: Spectrum-to-Signal Principle (SSP)
  - Supervised Fine-Tuning (SFT) for solution diversity
  - Reinforcement Learning (RL) for correct reasoning paths
- **Inference Engine**: Standard `transformers` library (PyTorch)
- **Token Efficiency**: Configurable thinking depth via prompt hints

### Hardware Requirements
- **Recommended**: Nvidia T4 - small (16 GB VRAM)
- **Memory Usage**: ~3-4 GB VRAM (1.5B params in float16)
- **Cost**: $0.40/hour on HuggingFace Spaces

### Implementation
```python
# Clean, simple transformers implementation
- torch.float16 for efficiency
- device_map="auto" for automatic GPU placement
- Repetition penalty (1.1) to reduce loops
- Automatic loop detection and truncation
```

## 📖 Usage Tips

### For Best Results:
1. **Frame problems competitively**: Clear input/output, edge cases, constraints
2. **Adjust thinking tokens**: 
   - 1024-2048 for quick, simple problems
   - 3072-4096 for standard algorithm challenges  
   - 6144-8192 for complex multi-step reasoning
3. **Use Python**: Model trained primarily on Python code
4. **Specify format**: Request specific output format (function, class, test cases)

### Example Prompts:
```
✅ Good: "Write a function to find the longest increasing subsequence. 
         Include time/space complexity analysis and test with [10,9,2,5,3,7,101,18]"

✅ Good: "Implement Dijkstra's algorithm with a min-heap. Handle disconnected graphs."

❌ Poor: "Debug my React app" (not its purpose)
❌ Poor: "How do I use pandas?" (limited library knowledge)
```

## 🔗 Resources

- **Model**: [WeiboAI/VibeThinker-1.5B](https://huggingface.co/WeiboAI/VibeThinker-1.5B)
- **Paper**: [arXiv:2511.06221](https://arxiv.org/abs/2511.06221)
- **GitHub**: [WeiboAI/VibeThinker](https://github.com/WeiboAI/VibeThinker)
- **License**: MIT

## 🙏 Credits

Developed by **WeiboAI**. This Space demonstrates the model with a clean interface and enhanced user experience.

## 📝 Citation

```bibtex
@article{vibethinker2025,
  title={Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B},
  author={WeiboAI Team},
  journal={arXiv preprint arXiv:2511.06221},
  year={2025}
}
```