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Llama 3 Patronus Lynx 8B Instruct Q4 K M GGUF
Llama-3-Patronus-Lynx-8B-Instruct-Q4_K_M-GGUF
L
Llama 3 Patronus Lynx 8B Instruct Q4 K M GGUF
AI ModelDevelopment & Tools#Large Language Model#Quantized Model#Natural Language Processing#Text GenerationStandard PicksOpen Source
Overview :
This model is a quantized large language model that utilizes 4-bit quantization technology to reduce storage and computational requirements. With 8.03 billion parameters, it is free for non-commercial use and ideal for high-performance language applications in resource-constrained environments.
Target Users :
The target audience includes researchers, developers, and businesses. Researchers use it for exploring natural language processing studies; developers find it useful for building related applications; and businesses can deploy it for text processing tasks on servers with limited internal resources.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 53.0K
Use Cases
1. Used in intelligent customer service systems to generate accurate text responses.
2. Assists content creation platforms in drafting articles, stories, and other creative texts.
3. Aids enterprise knowledge management systems in automating document summarization and question answering.
Features
1. Supports command line interface (CLI) inference via llama.cpp for convenient and quick operations.
2. Can build server inference with llama.cpp, facilitating service deployment.
3. Features 8.03 billion parameters, providing strong language understanding and generation capabilities to meet text processing needs.
4. Utilizes 4-bit quantization technology, optimizing storage and computational efficiency while saving resources.
5. Suitable for various natural language processing tasks such as text generation and question answering.
6. Based on a specific architecture, it can be easily used for inference through related methods.
7. Allows free use and sharing for non-commercial purposes, with clear usage limitations.
How to Use
1. Install llama.cpp via brew (suitable for Mac and Linux).
2. Use the llama-cli command line tool for inference, inputting parameters in the required format.
3. Set up llama-server for inference services, configuring the corresponding parameters.
4. You can also refer directly to the usage steps in the llama.cpp repository for inference.
5. Clone the llama.cpp repository, navigate to the directory, and build as instructed.
6. Run inference with the built binary file.
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