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rbehzadan/
ReaderLM-v2.gguf

Name: ReaderLM-v2.gguf
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ReaderLM-v2 GGUF Quantized Models for llama.cpp

This repository contains GGUF quantized versions of the ReaderLM-v2 model by Jina AI. These models are optimized for llama.cpp, making them efficient to run on CPUs and GPUs.

Model Information

ReaderLM-v2 is a 1.5 billion parameter model designed for HTML-to-Markdown and HTML-to-JSON conversion. It supports 29 languages and can handle up to 512,000 tokens in combined input and output length.

The model is useful for extracting structured data from web pages and various NLP applications.

Available Quantized Models

Model File	Quantization Type	Size	Description
`ReaderLM-v2-Q4_K_M.gguf`	Q4_K_M	986MB	Lower precision, optimized for CPU performance
`ReaderLM-v2-Q8_0.gguf`	Q8_0	1.6GB	Higher precision, better quality

These quantized versions balance performance and accuracy, making them suitable for different hardware setups.

Usage

Running the Model with llama.cpp

Clone and build llama.cpp:

bash
1git clone https://github.com/ggerganov/llama.cpp.git 2cd llama.cpp 3mkdir build && cd build 4cmake .. 5make -j$(nproc)
Run the model:

./llama-cli --model ReaderLM-v2-Q4_K_M.gguf --no-conversation --no-display-prompt --temp 0 --prompt '<|im_start|>system

Convert the HTML to Markdown. <|im_end|> <|im_start|>user

Hello, world!

<|im_end|> <|im_start|>assistant' 2>/dev/null ```

Replace ReaderLM-v2-Q4_K_M.gguf with ReaderLM-v2-Q8_0.gguf for better quality at the cost of performance.

Using the Model in Python with llama-cpp-python

pip install llama-cpp-python

python

model_path = "./models/ReaderLM-v2-Q4_K_M.gguf"
llm = Llama(model_path=model_path, chat_format="chatml")
output = llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "Convert the HTML to Markdown."},
        {
            "role": "user",
            "content": "<html><body><h1>Hello, world!</h1><p>This is a test!</p></body></html>"
        }
    ],
    temperature=0.1,
)

print(output['choices'][0]['message']['content'].strip())

Hardware Requirements

Q4_K_M (986MB): Runs well on CPUs with 8GB RAM or more
Q8_0 (1.6GB): Requires 16GB RAM for smooth performance

For GPU acceleration, compile llama.cpp with CUDA support.

Credits

Original Model: Jina AI - ReaderLM-v2
Quantization: Performed using llama.cpp

License

This model is released under Creative Commons Attribution-NonCommercial 4.0 (CC-BY-NC-4.0). See LICENSE for details.

Last updated: January 31, 2025

rbehzadan/ReaderLM-v2.gguf