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title: Diffusion Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: apache-2.0
---
# 🤖 Diffusion Chatbot
[](https://www.docker.com/)
[](https://www.python.org/)
Flask server hosting the **Qwen3-0.6B-diffusion-bd3lm-v0.1** model with real-time streaming inference. Watch diffusion language models generate text step-by-step!
## ✨ Features
- 🎯 **Real-time Streaming**: Watch the diffusion denoising process live
- 📡 **Three API Endpoints**: Simple generation, batch states, and SSE streaming
- ⚡ **GPU Support**: Automatic GPU detection with CPU fallback
- 🔄 **Progressive Generation**: See how different parts of text appear at different steps
## 📡 API Endpoints
### 1. Health Check
```bash
GET /health
```
### 2. Generate Text (Simple)
```bash
POST /generate
Content-Type: application/json
{
"prompt": "Your question here",
"max_new_tokens": 256
}
```
### 3. Generate with Real-time Streaming (SSE) ⭐
```bash
POST /generate_sse
Content-Type: application/json
{
"prompt": "Your question here",
"max_new_tokens": 100,
"capture_interval": 10
}
```
## 💡 Example Usage
```bash
# Simple generation
curl -X POST https://YOUR_USERNAME-diffusion-chatbot.hf.space/generate \
-H "Content-Type: application/json" \
-d '{"prompt": "Hello, how are you?", "max_new_tokens": 50}'
# Real-time streaming
curl -N -X POST https://YOUR_USERNAME-diffusion-chatbot.hf.space/generate_sse \
-H "Content-Type: application/json" \
-d '{"prompt": "Write a poem", "max_new_tokens": 100, "capture_interval": 10}'
```
## 🔧 Technical Details
| Component | Technology |
|-----------|------------|
| **Model** | [dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1) |
| **Framework** | Flask + PyTorch |
| **Method** | Block Diffusion Language Model (BD3LM) |
| **Base Model** | Qwen |
## ⚙️ Configuration
| Variable | Description | Default |
|----------|-------------|---------|
| `MODEL_NAME` | HuggingFace model name | `dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1` |
| `PORT` | Server port | `7860` |
## 🧠 How It Works
Unlike traditional language models that generate text left-to-right, diffusion language models:
1. Start with all tokens masked
2. Iteratively denoise over multiple steps
3. Generate different parts of text at different steps
4. Create a unique "thought process" visualization
## 📝 Notes
- Model downloads automatically on first run (~1.5GB)
- First request may be slow as model loads
- GPU is optional - automatic CPU fallback
- Lower `capture_interval` = more frequent updates
## 🙏 Acknowledgments
- Model: [dllm-hub](https://huggingface.co/dllm-hub)
- Framework: [dLLM](https://github.com/ZHZisZZ/dllm)
- Base: [Qwen](https://github.com/QwenLM/Qwen)
|