README.md

---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- lora
- qlora
- code
- html
- css
- javascript
- web-development
- peft
language:
- en
pipeline_tag: text-generation
---

# WebCoder-7B — Website Specialist LoRA

A QLoRA fine-tune of [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) specialized for generating complete, production-ready websites using HTML, CSS, and JavaScript.

## Model Details
- **Base model:** Qwen/Qwen2.5-Coder-7B-Instruct
- **Fine-tuning method:** QLoRA (4-bit)
- **LoRA rank:** 64
- **Precision:** bf16
- **Training samples:** 7,731
- **Epochs:** 3
- **Max length:** 1024 tokens

## Training Data
- Hoglet-33/webdev-coding-dataset
- sahil2801/CodeAlpaca-20k (web-filtered)
- HuggingFaceH4/CodeAlpaca_20K (web-filtered)
- HuggingFaceM4/WebSight (local cache)

## Usage

```python
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