125 lines
3.5 KiB
Markdown
125 lines
3.5 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-14B-Instruct
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tags:
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- n8n
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- workflow
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- automation
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- fine-tuned
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- code-generation
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- qlora
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datasets:
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- mbakgun/n8nbuilder-n8n-workflows-dataset
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pipeline_tag: text-generation
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language:
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- en
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library_name: mlx
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---
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# Qwen2.5-Coder-14B-n8n-Workflow-Generator
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Fine-tuned Qwen2.5-Coder-14B-Instruct model specialized for generating n8n workflow JSONs from natural language descriptions.
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## Model Description
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This model is a QLoRA fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the [n8nbuilder-n8n-workflows-dataset](https://huggingface.co/datasets/mbakgun/n8nbuilder-n8n-workflows-dataset), containing +2.5K n8n workflow templates.
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**Training Details:**
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- **Base Model**: Qwen/Qwen2.5-Coder-14B-Instruct
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- **Method**: QLoRA (4-bit quantization)
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- **LoRA Rank**: 32
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- **LoRA Alpha**: 64
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- **Training Steps**: 432 (3 epochs)
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- **Sequence Length**: 8192 tokens
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- **Learning Rate**: 2e-4
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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system_prompt = "You are an expert n8n workflow generation assistant. Your goal is to create valid, efficient, and functional n8n workflow configurations."
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user_input = "Create a workflow that monitors a RSS feed and sends new items to Discord."
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prompt = f"{system_prompt}\n\n{user_input}"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=4096,
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temperature=0.7,
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do_sample=True
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)
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workflow_json = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(workflow_json)
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```
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### MLX (Apple Silicon)
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```bash
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# Download MLX Q4 model
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mlx_lm.generate \
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--model mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator/mlx-q4 \
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--prompt "You are an expert n8n workflow generation assistant...\n\nCreate a workflow that sends Slack notifications when GitHub issues are created." \
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--max-tokens 4096
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```
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## Training Data
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This model was fine-tuned on the [n8nbuilder-n8n-workflows-dataset](https://huggingface.co/datasets/mbakgun/n8nbuilder-n8n-workflows-dataset), which contains:
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- **+2,304 workflow templates** (after filtering sequences >8192 tokens)
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- Format: Alpaca (instruction/input/output)
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- Source: n8n.io public template gallery
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- [n8nbuilder.dev - Create n8n Workflows in Seconds with AI](https://n8nbuilder.dev)
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## Performance
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- **Training Speed**: ~33.85s/step on H100 PCIe
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- **VRAM Usage**: ~30GB (4-bit QLoRA)
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- **Inference**: ~25-40 tok/s on Mac Mini M4 64GB (MLX)
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## Limitations
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- Generated workflows may require manual validation
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- Long workflows (>8192 tokens) may be truncated
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- Model trained on public templates only
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## Citation
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```bibtex
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@model{qwen25_coder_n8n_2025,
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title={Qwen2.5-Coder-14B-n8n-Workflow-Generator},
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author={mbakgun},
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year={2025},
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base_model={Qwen/Qwen2.5-Coder-14B-Instruct},
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dataset={mbakgun/n8nbuilder-n8n-workflows-dataset},
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url={https://huggingface.co/mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator}
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}
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```
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## Acknowledgments
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- [Qwen Team](https://huggingface.co/Qwen) for the base model
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- [n8n](https://n8n.io) for the workflow automation platform
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- [n8n-mcp](https://github.com/czlonkowski/n8n-mcp) for template indexing
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## License
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Apache 2.0
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