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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
def load_qwen_n8n_model():
"""Load the Qwen2.5-7B-n8n model with fallback tokenizer"""
model_name = "npv2k1/Qwen2.5-7B-n8n"
# Load tokenizer with fallback
try:
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
except:
print("Using base Qwen tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct", trust_remote_code=True)
# Load model
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
trust_remote_code=True
)
return tokenizer, model
def generate_n8n_workflow(tokenizer, model, prompt, max_length=1024):
"""Generate n8n workflow from prompt"""
formatted_prompt = f"Create an n8n workflow: {prompt}\n\nJSON:"
inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract JSON
json_start = result.find('{')
if json_start != -1:
return result[json_start:]
return result
# Usage
if __name__ == "__main__":
tokenizer, model = load_qwen_n8n_model()
workflow = generate_n8n_workflow(
tokenizer,
model,
"Send email when new GitHub issue is created"
)
print(workflow) |