Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...

This directory contains examples for BERT PTQ/QAT related training. mpirun -np 4 -H localhost:4 \ --allow-run-as-root -bind-to none -map-by slot \ -x NCCL_DEBUG=INFO ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Abstract: We construct a randomized vector quantizer which has a smaller maximum error compared to all known lattice quantizers with the same entropy for dimensions 5 ...

Running the example script llm-compressor/examples/quantization_w4a4_fp4/llama3_example.py results in a runtime error. Full traceback is included below.

With the rapid development of machine learning, Deep Neural Network (DNN) exhibits superior performance in solving complex problems like computer vision and natural language processing compared with ...

I'm diving deep into the intersection of infrastructure and machine learning. I'm fascinated by exploring scalable architectures, MLOps, and the latest advancements in AI-driven systems ...

Integral nonlinearity tracks the cumulative effects of an ADC’s differential nonlinearity. Figure 1. A three-bit ADC has an ideal step width of 1 LSB and a maximum ...

Post-training quantization (PTQ) focuses on reducing the size and improving the speed of large language models (LLMs) to make them more practical for real-world use. Such models require large data ...