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1. Serving Large models (part one): VLLM, LLAMA CPP Server, and SGLang

   This guide delves into three prominent projects for serving large language models and vision-language models: VLLM, LLAMA CPP Server, and SGLang. Each project offers distinct functionalities and is explained with usage instructions, features, and deployment methods.

   2024-09-30 Tags: vllm, llama cpp, llm by klotz
2. Llama.Cpp

   # obtain the original LLaMA model weights and place them in ./models
   ls ./models
   65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
   
   # install Python dependencies
   python3 -m pip install -r requirements.txt
   
   # convert the 7B model to ggml FP16 format
   python3 convert.py models/7B/
   
   # quantize the model to 4-bits (using q4_0 method)
   ./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0
   
   # run the inference
   ./main -m ./models/7B/ggml-model-q4_0.bin -n 128

   2023-06-05 Tags: github, llama, llama cpp, llm, self-hosted by klotz

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