A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor
In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor. We start with an FP16 baseline and then compare multiple compression strategies, including FP8 dynamic quantization, GPTQ W4A16, and SmoothQuant with GPTQ W8A8. Along the way, we benchmark each model variant for disk size, generation latency, throughput, perplexity, […] The post A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor appeared first on MarkTechPost.