README.md

---
library_name: peft
base_model: codellama/CodeLlama-13b-hf
tags:
- code-generation
- text-generation
- llama
- turkish
- n8n
- workflow
- automation
- fine-tuned
- lora
language:
- en
- tr
pipeline_tag: text-generation
widget:
- text: "Create an n8n workflow that triggers when a webhook receives data:"
  example_title: "n8n Webhook Workflow"
- text: '{"name": "HTTP Request", "type": "n8n-nodes-base.httpRequest", "parameters": {'
  example_title: "n8n HTTP Node"
- text: "n8n automation: monitor CSV file and send Slack notification:"
  example_title: "n8n File Monitor + Slack"
- text: "Build n8n workflow for API data processing:"
  example_title: "n8n API Processing"
inference: true
license: llama2
model_type: llama
---

# 🚀 Code Llama 13B - n8n Workflow Generator

<div align="center">

![Model Type](https://img.shields.io/badge/Model-LoRA%20Adapter-blue)
![Base Model](https://img.shields.io/badge/Base-CodeLlama%2013B-green)
![Specialization](https://img.shields.io/badge/Specialty-n8n%20Workflows-orange)
![License](https://img.shields.io/badge/License-Llama%202-red)

</div>

Bu model, **CodeLlama-13b-hf**'den fine-tune edilmiş, **n8n workflow automation** için özelleştirilmiş bir kod üretim modelidir.

## 🎯 Özelleştirilmiş Alanlar

- ✅ **n8n Workflow Creation** - Webhook, HTTP, API workflows
- ✅ **Node Configurations** - JSON node parameters  
- ✅ **Automation Logic** - File monitoring, data processing
- ✅ **Integration Patterns** - Slack, email, database integrations
- ✅ **Best Practices** - n8n terminology ve syntax

## 🚀 Hızlı Kullanım

### Widget Kullanımı
Yukarıdaki widget'ta örnek promptları deneyebilirsiniz!

### Kod ile Kullanım

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Base model yükle
base_model = AutoModelForCausalLM.from_pretrained(
    "codellama/CodeLlama-13b-hf",
    torch_dtype=torch.float16,
    device_map="auto"
)

# n8n fine-tuned adapter ekle
model = PeftModel.from_pretrained(base_model, "AlpYzc/code-llama-13b-turkish-quick-fix")

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("AlpYzc/code-llama-13b-turkish-quick-fix")

# n8n workflow üret
prompt = "Create an n8n workflow that triggers when a webhook receives data:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_new_tokens=150, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```

## 📊 Performance Comparison

| Model | n8n Terms | Workflow Focus | JSON Structure |
|-------|-----------|----------------|----------------|
| **Original CodeLlama** | ⭐⭐ | ⭐⭐ | ⭐⭐ |
| **n8n Fine-tuned** | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |

## 🎨 Example Outputs

### Input: "Create n8n webhook workflow:"

**Original CodeLlama:**
```
Create a n8n webhook workflow:
1. Add a webhook node
2. Create a webhook url
3. Update the n8n workflow with the webhook url
```

**n8n Fine-tuned:**
```
Create a webhook in n8n:
1. Create a new workflow.
2. Add a webhook node.
3. Copy the URL from the Webhook node to the clipboard.
4. Paste the URL into the N8N_WEBHOOK_URL field in the .env file.
```

## 🛠️ Training Details

- **Base Model**: `codellama/CodeLlama-13b-hf`
- **Method**: LoRA (Low-Rank Adaptation)
- **Training Data**: n8n workflow examples
- **Training Duration**: ~3.3 hours
- **Final Loss**: 0.1577
- **Parameters**: 250M adapter weights

## 🎯 Use Cases

### 1. **n8n Workflow Generation**
```python
prompt = "Create n8n workflow for monitoring file changes:"
# Generates complete n8n workflow with proper nodes
```

### 2. **Node Configuration**
```python
prompt = '{"name": "HTTP Request", "type": "n8n-nodes-base.httpRequest",'
# Generates valid n8n node JSON configuration
```

### 3. **Automation Patterns**
```python
prompt = "n8n automation: CSV processing and Slack notification:"
# Generates multi-step automation workflows
```

## ⚙️ Model Requirements

- **GPU Memory**: ~26GB (for full model)
- **RAM**: 32GB+ recommended
- **CUDA**: 11.8+
- **Python**: 3.8+
- **Dependencies**: `transformers`, `peft`, `torch`

## 🔗 Related Links

- **Base Model**: [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf)
- **n8n Documentation**: [n8n.io](https://n8n.io)
- **LoRA Paper**: [LoRA: Low-Rank Adaptation](https://arxiv.org/abs/2106.09685)

## 📜 Citation

```bibtex
@misc{code-llama-n8n-2025,
  title={Code Llama 13B n8n Workflow Generator},
  author={AlpYzc},
  year={2025},
  url={https://huggingface.co/AlpYzc/code-llama-13b-turkish-quick-fix}
}
```

## ⚠️ Limitations

- Specialized for n8n workflows - may not perform well on general coding tasks
- Requires significant GPU memory for full model inference
- LoRA adapter needs base model for functionality
- Output quality depends on prompt specificity

## 🤝 Contributing

Bu model n8n community için geliştirilmiştir. Feedback ve improvement önerileri memnuniyetle karşılanır!

---

<div align="center">

**🚀 Ready to automate your workflows with n8n?**

[![Use with Transformers](https://img.shields.io/badge/🤗%20Transformers-Use%20Model-yellow)](https://huggingface.co/AlpYzc/code-llama-13b-turkish-quick-fix)
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_generation.ipynb)

</div>