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Vinitrajputt
/
COT-html-lamma

PEFT
Safetensors
GGUF
llama
Model card Files
xet
 Community
How to use from
llama.cpp
Install from brew 
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Vinitrajputt/COT-html-lamma:
# Run inference directly in the terminal:
llama-cli -hf Vinitrajputt/COT-html-lamma:
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Vinitrajputt/COT-html-lamma:
# Run inference directly in the terminal:
llama-cli -hf Vinitrajputt/COT-html-lamma:
Use pre-built binary 
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Vinitrajputt/COT-html-lamma:
# Run inference directly in the terminal:
./llama-cli -hf Vinitrajputt/COT-html-lamma:
Build from source code 
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Vinitrajputt/COT-html-lamma:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Vinitrajputt/COT-html-lamma:
Use Docker 
docker model run hf.co/Vinitrajputt/COT-html-lamma:
Quick Links

✨ COT-HTML-Llama: Weaving HTML with Words 🦙

Transform natural language into beautiful, dynamic HTML with COT-HTML-Llama, a finetuned Llama 3.1 7b model! 🪄 Trained on a Groq-enhanced Alpaca dataset, this model uses Chain-of-Thought (CoT) reasoning to craft interactive web experiences. Get creative and code-free – let your words build the web! 🌐

🌟 Model Magic:

COT-HTML-Llama isn't just about static HTML. It's about bringing your web visions to life! ✨

  • Dynamic HTML Generation: Turn text instructions into working HTML, complete with internal CSS and JavaScript.
  • Chain-of-Thought Reasoning: Watch the model think step-by-step, translating your ideas into structured code. 🧠
  • Interactive Elements: Create buttons that change color, dynamic text, and more – all from simple prompts!
  • Strawberry Superstar: This model even conquers the infamous "Strawberry Challenge," accurately counting the "r"s – a testament to its improved logical reasoning! 🍓

🚀 Use Cases:

Unleash your inner web developer with ease:

  • Quick Prototyping: Mock up web page ideas in seconds.
  • Content Creation: Generate engaging web content without writing a single line of code.
  • Learning HTML: Explore HTML generation through a new, intuitive lens.

🚧 Limitations:

While COT-HTML-Llama is powerful, it's still learning:

  • Complex Layouts: Intricate designs might still pose a challenge.
  • External Resources: Currently supports only internal CSS and JavaScript. No external images or scripts (yet!).
  • Ambiguity: Highly nuanced instructions might need extra clarification.

🛠️ Training & Usage:

  • Dataset: Groq-transformed Alpaca dataset, split & merged for optimal training.
  • Finetuning: Unsloth technique for peak performance. 💪
  • Quantized Versions: Q4_K_M, Q5_K_M, Q8_0 for efficient inference.
  • Hugging Face Hub: Get the model and code here: https://huggingface.co/Vinitrajputt/COT-html-lamma

✨ Future Enhancements:

We're constantly improving COT-HTML-Llama:

  • Robust Error Handling: Smoother sailing ahead!
  • Advanced Prompting: Even more control over your HTML.
  • Automated Evaluation: Measuring the magic.
  • Model Optimization: Faster and better HTML generation.

🤝 Contribute:

Join us in building the future of HTML generation! Contributions are welcome! Let's make some web magic together! ✨

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Architecture
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