Step-by-step guide on how to accelerate large language models. Deployment of Large Language Models and the use of tools like vLLM and quantization. Measurement of latency and throughput. Installation and usage of vLLM. Comparison of vLLM and Hugging Face transformers. Deployment of A Large Language Model with vLLM. Benchmarking Latency and Throughput in Real Time. Quantization of Large Language Models and the use of BitsandBytes library.
Table of contents
Step-by-step guide on how to accelerate large language modelsDeployment of Large Language Models (LLMs)Latency and ThroughputInstall Required PackagesWhat is Phi-2?Benchmarking LLM Latency and Throughput with Hugging Face TransformersGenerated OutputHow vLLM worksvLLM: Easy, Fast, and Cheap LLM Serving with PagedAttentionRun Phi-2 with vLLMGenerated OutputBenchmarking Latency and Throughput in Real TimeGoogle ColaboratoryBitsandBytesQuantization of Mistral 7B ModelNF4(4-bit Normal Float) and Double QuantizationQLoRA: Efficient Finetuning of Quantized LLMsGenerated OutputGoogle ColaboratoryConclusionvLLM: Easy, Fast, and Cheap LLM Serving with PagedAttentionMaking LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRAUnderstanding performance benchmarks for LLM inferenceWhat are Quantized LLMs?16 Impressions