from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base, "lhordking/webcoder-7b")

prompt = "Create a responsive dark mode landing page for a SaaS product"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Example Prompts

"Create a responsive navbar with dark mode toggle"
"Build a SaaS landing page with hero section and pricing table"
"Make a login form with email and password validation"
"Create a portfolio page with project cards and animations"

Limitations

Best results with HTML/CSS/JS prompts
Output quality improves with specific, detailed prompts
May need more training data for complex full-stack applications