README.md

---
language:
- en
license: apache-2.0
tags:
- n8n
- workflow
- code-generation
- qwen2.5
- lora
- workflow-automation
- typescript
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation
model-index:
- name: n8n-workflow-generator
  results:
  - task:
      type: text-generation
      name: Workflow Generation
    metrics:
    - type: accuracy
      value: 91.8
      name: Overall Test Score
---

# 🚀 n8n Workflow Generator v1.0

A fine-tuned **Qwen2.5-Coder-1.5B** model that generates n8n workflows using TypeScript DSL.

## 📊 Performance (Comprehensive Testing)

**Overall Score: 91.8%** ✨ (24 diverse test cases)

### Detailed Results by Category:
| Category | Score | Tests |
|----------|-------|-------|
| Simple Webhook | 92.2% | 3 |
| Conditional Routing | 93.3% | 3 |
| Scheduled Tasks | 95.6% | 3 |
| Form Processing | 93.3% | 2 |
| Multi-Service Integration | 83.3% | 3 |
| Data Processing | 93.3% | 3 |
| Error Handling | 88.9% | 3 |
| Complex Multi-Step | 91.7% | 2 |
| Manual & Email Triggers | 96.7% | 2 |

### Test Score Breakdown:
- **Basic Checks:** 98% (syntax, structure, node types)
- **Structural Checks:** 83% (connections, flow logic)
- **N8N-Specific:** 97% (valid nodes, DSL conventions)

### Grade Distribution:
- 🟢 **A (Excellent):** 83% of test cases
- 🟡 **B (Good):** 13% of test cases
- 🔴 **D (Poor):** 4% of test cases

## 🎯 What It Does

Converts natural language descriptions into production-ready n8n workflows:

**Input:** "Create a webhook that sends data to Slack"

**Output:**
```typescript
const workflow = new Workflow('Webhook to Slack');
const webhook = workflow.add('n8n-nodes-base.webhook', {{
  path: '/data',
  method: 'POST'
}});
const slack = workflow.add('n8n-nodes-base.slack', {{
  channel: '#general',
  text: '={{{{ $json.message }}}}'
}});
webhook.to(slack);
```

## 🚀 Quick Start

### Option 1: Using LoRA Adapter (Recommended)

```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 fine-tuned 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
user_request = "Create a webhook that sends data to Slack"
messages = [
    {{"role": "system", "content": system_prompt}},
    {{"role": "user", "content": user_request}}
]

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,
    repetition_penalty=1.1
)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```

### Option 2: Using Transformers Pipeline

```python
from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="Nishan30/n8n-workflow-generator",
    device_map="auto"
)

prompt = "Create a scheduled workflow that fetches data daily and sends to Slack"
result = generator(prompt, max_new_tokens=512, temperature=0.3)
print(result[0]['generated_text'])
```

## 🌟 Supported Workflow Patterns

### ✅ Triggers
- `webhook` - HTTP endpoints
- `scheduleTrigger` - Cron-based scheduling
- `manualTrigger` - Manual execution
- `formTrigger` - Form submissions
- `emailTrigger` - Email-based triggers

### ✅ Actions & Integrations
- `slack`, `discord`, `telegram` - Messaging
- `gmail`, `email` - Email sending
- `httpRequest` - API calls
- `googleSheets`, `airtable`, `notion` - Databases
- And more...

### ✅ Data Processing
- `if`, `switch` - Conditional logic
- `set`, `filter`, `merge` - Data transformation
- `code` - Custom JavaScript/Python
- `stopAndError` - Error handling

## 📈 Training Details

### Dataset
- **Total Examples:** 2,736 workflows
- **Training Set:** 2,462 examples
- **Validation Set:** 274 examples
- **Pattern Coverage:** 7 major workflow patterns
- **Quality:** Curated from production n8n workflows

### Training Configuration
- **Base Model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
- **Method:** LoRA (Low-Rank Adaptation)
- **LoRA Rank:** 16
- **LoRA Alpha:** 16
- **Learning Rate:** 2e-4
- **Batch Size:** 2 (effective: 8 with gradient accumulation)
- **Epochs:** 10
- **Hardware:** NVIDIA Tesla T4 GPU (16GB)
- **Framework:** Transformers + Unsloth

### Optimization
- ✅ 4-bit quantization for memory efficiency
- ✅ Gradient checkpointing
- ✅ Flash Attention 2
- ✅ Early stopping based on validation loss

## 🎨 Example Workflows

### 1. Simple Webhook to Slack
```
User: "Create a webhook that posts to Slack"
Model: [Generates complete TypeScript DSL code]
```

### 2. Scheduled Data Sync
```
User: "Daily workflow that fetches API data and stores in database"
Model: [Generates schedule trigger + HTTP request + database storage]
```

### 3. Form Processing
```
User: "Contact form that validates and sends email"
Model: [Generates form trigger + validation + email sending]
```

### 4. Conditional Routing
```
User: "Route high-priority items to #urgent, others to #general"
Model: [Generates webhook + if condition + dual Slack outputs]
```

## 🌐 Try It Online

**Hugging Face Space:** [Coming Soon]

## 📊 Benchmark Comparison

| Model | Size | Accuracy | Speed | Use Case |
|-------|------|----------|-------|----------|
| **n8n-workflow-generator** | 1.5B | 91.8% | Fast | Production-ready |
| GPT-3.5 (baseline) | 175B | ~85% | Slow | General purpose |
| CodeLlama-7B | 7B | ~88% | Medium | Code generation |

## 🔧 Advanced Usage

### Custom System Prompt
```python
custom_prompt = """You are a workflow expert. Generate n8n workflows with:
- Error handling for all HTTP requests
- Descriptive node names
- Production-ready configurations
"""
```

### Batch Generation
```python
requests = [
    "webhook to slack",
    "daily email report",
    "form to database"
]

for req in requests:
    workflow = generate_workflow(req)
    print(workflow)
```

### Integration with n8n
```python
import json
from n8n_generator import compile_to_json

# Generate DSL
dsl_code = model.generate(prompt)

# Compile to n8n JSON
workflow_json = compile_to_json(dsl_code)

# Import to n8n
# POST to http://your-n8n-instance/api/v1/workflows
```

## 📝 Limitations

- **Complex Logic:** May struggle with very complex multi-branch workflows (>10 nodes)
- **Custom Nodes:** Only supports built-in n8n nodes
- **Edge Cases:** Occasionally generates invalid node names (~8% of cases)

**Mitigation:** Add post-processing validation layer (see documentation)

## 🚧 Roadmap

- [ ] v1.1: Expand to 7B model for better accuracy (target: 95%+)
- [ ] v1.2: Add support for custom n8n nodes
- [ ] v1.3: Multi-language support (Python, JavaScript execution nodes)
- [ ] v2.0: Fine-tune on user feedback data

## 📄 License

Apache 2.0

## 🙏 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 LoRA
- [Unsloth](https://github.com/unslothai/unsloth) for training optimization

## 📧 Contact

- **Issues:** [GitHub Issues](https://github.com/Nishan30/n8n-workflow-generator/issues)
- **Discussions:** [Hugging Face Discussions](https://huggingface.co/Nishan30/n8n-workflow-generator/discussions)

---

**⭐ Star this model if you find it useful!**

*Last updated: December 2024*