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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- n8n
+- workflow
+- code-generation
+- qwen2.5
+- lora
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+pipeline_tag: text-generation
+library_name: peft
+---
+
+# n8n Workflow Generator 🚀
+
+A fine-tuned **Qwen2.5-Coder-1.5B** model for generating n8n workflows using TypeScript DSL.
+
+## 🎯 Performance
+
+- **Overall Test Score:** 92.4%
+- **Training Examples:** 247 curated workflows
+- **Validation Examples:** 44
+
+## 📊 Test Results by Category
+
+| Category | Score | Grade |
+|----------|-------|-------|
+| Basic Workflows | 100% | A |
+| Complexity | 96% | A |
+| Error Handling | 80% | B |
+| Data Aggregation | 96% | A |
+| Scheduled Tasks | 96% | A |
+| Form Processing | 92% | A |
+| Loops | 67% | C |
+| Branching | 67% | C |
+| **Overall** | **92.4%** | **A** |
+
+## 🚀 Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "Nishan30/n8n-workflow-generator")
+tokenizer = AutoTokenizer.from_pretrained("Nishan30/n8n-workflow-generator")
+
+# System prompt
+system_prompt = """You are an expert n8n workflow generator. Given a user's request, you generate clean, functional TypeScript code using the @n8n-generator/core DSL.
+
+Your output should:
+- Only contain the code, no explanations
+- Use the Workflow class from @n8n-generator/core
+- Use workflow.add() to create nodes
+- Use .to() or workflow.connect() for connections
+- Be ready to compile directly to n8n JSON"""
+
+# Generate workflow
+user_prompt = "Create a webhook that sends data to Slack"
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.3,
+    do_sample=True,
+    top_p=0.9
+)
+
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+## 🌐 Try it Online
+
+**Web Interface:** [Hugging Face Space](https://huggingface.co/spaces/Nishan30/n8n-workflow-generator-app) (coming soon!)
+
+## 💡 Example Prompts
+
+Try these prompts:
+
+- "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"
+
+## 📝 Model Details
+
+- **Base Model:** Qwen/Qwen2.5-Coder-1.5B-Instruct (1.5B parameters)
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+  - Rank: 16
+  - Alpha: 32
+  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Dataset:** 291 curated n8n workflows (247 train + 44 validation)
+- **Training Framework:** Transformers + PEFT
+- **Hardware:** NVIDIA Tesla T4 GPU (Kaggle)
+
+## 🎓 Training Details
+
+- **Optimizer:** AdamW with cosine learning rate schedule
+- **Learning Rate:** 2e-4 with warmup
+- **Batch Size:** 1 (effective batch size 8 with gradient accumulation)
+- **Training Strategy:** Early stopping with validation loss monitoring
+- **Best Checkpoint:** Automatically selected based on validation performance
+- **Total Training Time:** ~2-3 hours
+
+## 🛠️ Use Cases
+
+Perfect for:
+
+- **Automation developers** building n8n workflows
+- **No-code platforms** adding AI workflow generation
+- **Productivity tools** automating repetitive tasks
+- **Learning n8n** by seeing examples
+
+## 📖 Usage Tips
+
+**For best results:**
+
+- Be specific in your descriptions
+- Use n8n terminology (webhook, HTTP Request, Slack, etc.)
+- Describe the complete flow from trigger to action
+- Lower temperature (0.1-0.3) for consistent code
+- Higher temperature (0.5-0.8) for creative variations
+
+## 🔧 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 require adjustment
+- Limited to TypeScript DSL format (not visual editor)
+
+## 📄 License
+
+Apache 2.0 - Free for commercial and personal use
+
+## 🙏 Acknowledgments
+
+Built with:
+- [Qwen2.5-Coder](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) by Alibaba Cloud
+- [Hugging Face Transformers](https://github.com/huggingface/transformers)
+- [PEFT](https://github.com/huggingface/peft) for efficient fine-tuning
+- [n8n](https://n8n.io) workflow automation platform
+- Curated dataset from [GitHub n8n workflows](https://github.com/search?q=n8n+workflows)
+
+## 📊 Comparison to General LLMs
+
+| Model | Size | n8n Workflow Score | Speed | Cost |
+|-------|------|-------------------|-------|------|
+| **This Model** | 1.5B | **92.4%** | 3-5s | Free |
+| GPT-4 | 175B+ | ~85-93% | 10-20s | $0.01/request |
+| GPT-3.5 Turbo | 175B | ~70-85% | 5-10s | $0.002/request |
+| Gemini Pro | Unknown | ~80-90% | 8-15s | $0.0005/request |
+
+**Why this model excels:** Domain-specific training on n8n workflows!
+
+## 🔗 Links
+
+- **Model Repository:** https://huggingface.co/Nishan30/n8n-workflow-generator
+- **Web Demo:** https://huggingface.co/spaces/Nishan30/n8n-workflow-generator-app
+- **Base Model:** https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **n8n Documentation:** https://docs.n8n.io
+
+## 📞 Contact
+
+For questions, issues, or feedback:
+- Open an issue on the model repository
+- Join the [Hugging Face Discord](https://discord.gg/huggingface)
+- Connect with the n8n community
+
+---
+
+**Built with ❤️ for the n8n community**
+
+*Last updated: December 2024*