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| title: n8n Workflow Generator | |
| emoji: 🚀 | |
| colorFrom: red | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.16.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # n8n Workflow Generator | |
| Generate n8n workflows using natural language! This app uses a fine-tuned **Qwen2.5-Coder-1.5B** model to convert plain English descriptions into working n8n workflows. | |
| ## 🎯 Performance | |
| - **Overall Test Score:** 92.4% | |
| - **Training Examples:** 247 curated workflows | |
| - **Validation Examples:** 44 | |
| ## 🚀 Features | |
| - **Natural Language Input:** Describe workflows in plain English | |
| - **Visual Preview:** See your workflow nodes and connections | |
| - **TypeScript DSL:** Get clean, production-ready code | |
| - **n8n JSON Export:** Import directly into your n8n instance | |
| - **Adjustable Parameters:** Control creativity and output length | |
| ## 📊 Test Results by Category | |
| | Category | Score | | |
| |----------|-------| | |
| | Basic Workflows | 100% | | |
| | Complexity | 96% | | |
| | Error Handling | 80% | | |
| | Loops | 67% | | |
| | Branching | 67% | | |
| | **Overall** | **92.4%** | | |
| ## 💡 Example Prompts | |
| Try these prompts to see what the model can do: | |
| - "Create a webhook that sends data to Slack" | |
| - "Schedule that runs daily and backs up database to Google Drive" | |
| - "Webhook receives form data, validates email, saves to Airtable" | |
| - "Monitor RSS feed and post new items to Twitter" | |
| - "Fetch GitHub issues, if priority is high send to Slack, else email" | |
| ## 🛠️ How It Works | |
| 1. **Input:** You describe your workflow in natural language | |
| 2. **Generation:** Fine-tuned Qwen2.5-Coder-1.5B generates TypeScript DSL | |
| 3. **Conversion:** Code is converted to n8n JSON format | |
| 4. **Visualization:** Workflow structure is displayed visually | |
| 5. **Export:** Copy and import into your n8n instance | |
| ## 📝 Model Details | |
| - **Base Model:** Qwen/Qwen2.5-Coder-1.5B-Instruct | |
| - **Fine-tuning Method:** LoRA (Low-Rank Adaptation) | |
| - **Dataset:** Curated n8n workflows from GitHub | |
| - **Training Framework:** Transformers + PEFT | |
| ## 🎓 Training Details | |
| - **LoRA Rank:** 16 | |
| - **LoRA Alpha:** 32 | |
| - **Learning Rate:** 2e-4 | |
| - **Training Strategy:** Early stopping with validation monitoring | |
| - **Hardware:** NVIDIA Tesla T4 GPU | |
| ## 📖 Usage Tips | |
| - **Be specific:** More details = better results | |
| - **Use n8n terminology:** Mention specific nodes like "webhook", "Slack", "HTTP Request" | |
| - **Describe the flow:** "When X happens, do Y, then Z" | |
| - **Adjust temperature:** Lower (0.1-0.3) for consistency, higher (0.5-0.8) for creativity | |
| ## 🔧 Limitations | |
| - Works best with common n8n patterns | |
| - May struggle with very complex branching (>5 conditions) | |
| - Advanced error handling might need manual refinement | |
| - Custom node configurations may need adjustment | |
| ## 📄 License | |
| Apache 2.0 | |
| ## 🙏 Acknowledgments | |
| Built with: | |
| - [Qwen2.5-Coder](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) | |
| - [Hugging Face Transformers](https://github.com/huggingface/transformers) | |
| - [PEFT](https://github.com/huggingface/peft) | |
| - [Gradio](https://gradio.app) | |