eclaude/qwen-coder-3b-n8n-sft

Qwen Coder 3B - n8n Workflow Generation (SFT)

A fine-tuned version of Qwen2.5-Coder-3B-Instruct specialized in generating n8n workflow JSON configurations from natural language descriptions.

Model Description

This model was fine-tuned using Supervised Fine-Tuning (SFT) with the TRL library to understand and generate n8n workflow structures. It can produce valid JSON workflow definitions including nodes, connections, and configuration parameters.

Property	Value
Base Model	Qwen/Qwen2.5-Coder-3B-Instruct
Training Method	SFT (Supervised Fine-Tuning)
Parameters	3B
Training Data	eclaude/n8n-workflows-sft
License	Apache 2.0

Intended Uses

Direct Use

• Generate n8n workflow JSON from natural language descriptions
• Assist developers in creating automation workflows
• Prototype workflow structures before manual refinement

Out-of-Scope Use

• Production-critical workflow generation without human review
• General-purpose code generation (use the base model instead)
• Non-n8n automation platforms

Quick Start

Installation

pip install transformers torch accelerate

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "eclaude/qwen-coder-3b-n8n-sft"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Create an n8n workflow that fetches data from a REST API every hour and sends a Slack notification"

messages = [
    {"role": "system", "content": "You are an n8n workflow expert. Generate valid n8n workflow JSON configurations."},
    {"role": "user", "content": 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=2048,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Streaming Generation

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "eclaude/qwen-coder-3b-n8n-sft"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "Create a workflow to monitor a GitHub repository for new issues and create Trello cards"

messages = [
    {"role": "system", "content": "You are an n8n workflow expert. Generate valid n8n workflow JSON configurations."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

_ = model.generate(
    **inputs,
    max_new_tokens=2048,
    streamer=streamer,
    temperature=0.7,
    do_sample=True
)

Using with vLLM (Recommended for Production)

from vllm import LLM, SamplingParams

llm = LLM(model="eclaude/qwen-coder-3b-n8n-sft")

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048
)

prompt = """<|im_start|>system
You are an n8n workflow expert. Generate valid n8n workflow JSON configurations.<|im_end|>
<|im_start|>user
Create a workflow to sync Airtable records with a PostgreSQL database daily<|im_end|>
<|im_start|>assistant
"""

outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)

Quantized Inference (Low VRAM)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_id = "eclaude/qwen-coder-3b-n8n-sft"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4"
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)

# Use same inference code as above

Training Details

Training Data

The model was fine-tuned on eclaude/n8n-workflows-sft, a curated dataset of n8n workflow examples with natural language descriptions.

Training Procedure

• Framework: TRL SFTTrainer
• Infrastructure: Hugging Face Jobs
• Precision: Mixed precision (bf16)

Limitations

• JSON Validity: Generated workflows may require manual validation and adjustment
• Node Coverage: Performance varies by n8n node type; common integrations (HTTP, Slack, GitHub) perform better than niche nodes
• Complex Workflows: Multi-branch workflows with complex logic may be incomplete
• Version Drift: n8n workflow schema evolves; generated JSON may need updates for newer n8n versions
• No Execution Testing: Outputs are not tested against a live n8n instance

Recommendations

1. Always validate generated JSON before importing into n8n
2. Use as a starting point, not a complete solution
3. Review connection mappings and credentials placeholders
4. Test workflows in a staging environment first

Example Output

Prompt: "Create a simple webhook trigger that logs incoming data"

Generated Workflow:

{
  "name": "Webhook Logger",
  "nodes": [
    {
      "parameters": {
        "path": "webhook-log",
        "responseMode": "onReceived",
        "responseData": "allEntries"
      },
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "typeVersion": 1,
      "position": [250, 300]
    },
    {
      "parameters": {
        "functionCode": "console.log(JSON.stringify($input.all(), null, 2));\nreturn $input.all();"
      },
      "name": "Log Data",
      "type": "n8n-nodes-base.function",
      "typeVersion": 1,
      "position": [450, 300]
    }
  ],
  "connections": {
    "Webhook": {
      "main": [[{"node": "Log Data", "type": "main", "index": 0}]]
    }
  }
}

Citation

@misc{qwen-coder-n8n-sft-2025,
  author = {eclaude},
  title = {Qwen Coder 3B - n8n Workflow Generation (SFT)},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/eclaude/qwen-coder-3b-n8n-sft}
}

Model Card Contact

For questions, issues, or contributions, please open a discussion on this repository or contact via Hugging Face.