KalnRangelov/landing-page-gen-1.5b

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:

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.

Full Experiment

See the full experiment writeup, code, and example outputs on GitHub: KalnRangelov/LLM-Landing-page-distillation

License

MIT