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