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