Llama-3-Patronus-Lynx-70B-Instruct-Q4_K_M-GGUF

A large quantized language model with 70 billion parameters.

CommonProductProgrammingNatural Language ProcessingTransformers

PatronusAI/Llama-3-Patronus-Lynx-70B-Instruct-Q4_K_M-GGUF is a large quantized language model based on 70 billion parameters, utilizing 4-bit quantization technology to reduce model size and enhance inference efficiency. This model belongs to the PatronusAI series and is built upon the Transformers library, suitable for applications requiring high-performance natural language processing. The model adheres to the cc-by-nc-4.0 license agreement, allowing for non-commercial usage and sharing.

Supports natural language understanding and generation tasks such as text summarization
question answering
and text generation.
Utilizes 4-bit quantization technology to reduce model size and improve inference speed.
Compatible with llama.cpp
deployable and usable via command line tools or server mode.
With 70.6 billion parameters
it provides powerful language understanding and generation capabilities.
Available for direct download and usage through Hugging Face platform.
Ideal for high-performance natural language processing needs in research and commercial projects.

\The target audience includes researchers
developers
and enterprise users in the field of natural language processing. With numerous model parameters
it is well-suited for handling complex language tasks such as text generation
translation

Used in chatbots to provide a smooth conversational experience.
Applied in content creation for automated article or story generation.
Assists in the educational domain by aiding language learning and providing personalized learning suggestions.

1. Install llama.cpp: Install the llama.cpp tool via brew.
2. Clone the llama.cpp repository: Use git to clone the llama.cpp project.
3. Build llama.cpp: Navigate to the project directory and build the project using the LLAMA_CURL=1 flag.
4. Run inference: Use the llama-cli or llama-server command line tools to specify the model and input parameters
and execute the inference task.

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5.2

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