75 lines
2.2 KiB
Markdown
75 lines
2.2 KiB
Markdown
---
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license: mit
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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tags:
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- landing-page
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- html
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- css
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- web-development
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- distillation
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- lora
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- gguf
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language:
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- en
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pipeline_tag: text-generation
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---
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# Landing Page Generator 1.5B
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A fine-tuned Qwen2.5-Coder-1.5B model that generates complete, single-file HTML landing pages with embedded CSS.
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Trained via **knowledge distillation** from DeepSeek V3 (685B) using LoRA on Apple Silicon.
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## Model Variants
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| File | Precision | Size | Description |
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|------|-----------|------|-------------|
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| `model-f16.gguf` | FP16 | 3.1 GB | Full precision, best quality |
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| `model-q8.gguf` | Q8_0 | 1.6 GB | 8-bit quantized, near-identical quality |
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| `model-q4.gguf` | Q4_K_M | 986 MB | 4-bit quantized, good quality, smallest |
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## Usage with Ollama
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1. Download a GGUF file
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2. Create a `Modelfile`:
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```
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FROM ./model-q8.gguf
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TEMPLATE """{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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<|im_start|>assistant
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"""
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PARAMETER stop "<|im_end|>"
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PARAMETER temperature 0.3
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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."
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```
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3. Import and run:
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```bash
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ollama create landing-page-gen -f Modelfile
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ollama run landing-page-gen "Create a landing page for a space tourism company called Orbit Adventures"
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```
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## Training Details
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- **Base model**: Qwen2.5-Coder-1.5B-Instruct (4-bit)
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- **Teacher model**: DeepSeek V3 (685B parameters)
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- **Method**: LoRA (rank 16, 0.3% of weights trainable)
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- **Training data**: 500 diverse landing pages generated by DeepSeek V3
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- **Training**: 600 iterations on Apple Silicon (M-series) using MLX
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- **Best validation loss**: 0.218
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## Training Data
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The training dataset is available at [KalnRangelov/landing-page-training-data](https://huggingface.co/datasets/KalnRangelov/landing-page-training-data).
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## Full Experiment
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See the full experiment writeup, code, and example outputs on GitHub: [KalnRangelov/LLM-Landing-page-distillation](https://github.com/KalinRangelovRangelov/LLM-Landing-page-distillation)
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## License
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MIT
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