---
license: mit
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
  - landing-page
  - html
  - css
  - web-development
  - distillation
  - lora
  - gguf
language:
  - en
pipeline_tag: text-generation
---

# Landing Page Generator 1.5B

A fine-tuned Qwen2.5-Coder-1.5B model that generates complete, single-file HTML landing pages with embedded CSS.

Trained via **knowledge distillation** from DeepSeek V3 (685B) using LoRA on Apple Silicon.

## Model Variants

| File | Precision | Size | Description |
|------|-----------|------|-------------|
| `model-f16.gguf` | FP16 | 3.1 GB | Full precision, best quality |
| `model-q8.gguf` | Q8_0 | 1.6 GB | 8-bit quantized, near-identical quality |
| `model-q4.gguf` | Q4_K_M | 986 MB | 4-bit quantized, good quality, smallest |

## Usage with Ollama

1. Download a GGUF file
2. Create a `Modelfile`:

```
FROM ./model-q8.gguf
TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.3
SYSTEM "You are a web developer. When asked to create a landing page, output a complete single-file HTML document with embedded CSS and modern design. Use clean gradients, card layouts, and responsive design. Output only the HTML code, nothing else."
```

3. Import and run:
```bash
ollama create landing-page-gen -f Modelfile
ollama run landing-page-gen "Create a landing page for a space tourism company called Orbit Adventures"
```

## Training Details

- **Base model**: Qwen2.5-Coder-1.5B-Instruct (4-bit)
- **Teacher model**: DeepSeek V3 (685B parameters)
- **Method**: LoRA (rank 16, 0.3% of weights trainable)
- **Training data**: 500 diverse landing pages generated by DeepSeek V3
- **Training**: 600 iterations on Apple Silicon (M-series) using MLX
- **Best validation loss**: 0.218

## Training Data

The training dataset is available at [KalnRangelov/landing-page-training-data](https://huggingface.co/datasets/KalnRangelov/landing-page-training-data).

## Full Experiment

See the full experiment writeup, code, and example outputs on GitHub: [KalnRangelov/LLM-Landing-page-distillation](https://github.com/KalinRangelovRangelov/LLM-Landing-page-distillation)

## License

MIT