Instructions to use Vinitrajputt/COT-html-lamma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vinitrajputt/COT-html-lamma with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Vinitrajputt/COT-html-lamma")

llama-cpp-python

How to use Vinitrajputt/COT-html-lamma with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Vinitrajputt/COT-html-lamma",
	filename="unsloth.BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Vinitrajputt/COT-html-lamma with 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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Vinitrajputt/COT-html-lamma:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Vinitrajputt/COT-html-lamma:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Vinitrajputt/COT-html-lamma:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Vinitrajputt/COT-html-lamma:Q4_K_M

Use Docker

docker model run hf.co/Vinitrajputt/COT-html-lamma:Q4_K_M

LM Studio
Jan
Ollama
How to use Vinitrajputt/COT-html-lamma with Ollama:
```
ollama run hf.co/Vinitrajputt/COT-html-lamma:Q4_K_M
```

Unsloth Studio new

How to use Vinitrajputt/COT-html-lamma with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Vinitrajputt/COT-html-lamma to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Vinitrajputt/COT-html-lamma to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Vinitrajputt/COT-html-lamma to start chatting

Docker Model Runner
How to use Vinitrajputt/COT-html-lamma with Docker Model Runner:
```
docker model run hf.co/Vinitrajputt/COT-html-lamma:Q4_K_M
```

Lemonade

How to use Vinitrajputt/COT-html-lamma with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Vinitrajputt/COT-html-lamma:Q4_K_M

Run and chat with the model

lemonade run user.COT-html-lamma-Q4_K_M

List all available models

lemonade list

✨ 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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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

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