low-bit quantization

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papersSEP 11 04:00 UTC

RDQ quantization method targets accuracy loss below 4-bit in LLMs

A research paper proposes Residual Distribution Quantization, a post-training quantization approach for large language models. The authors attribute the sharp accuracy drop seen below 4-bit precision to distributional drift in the residual stream, where quantization error introduced at each transformer layer builds up in the shared representation. Their method aims to correct this accumulated error to preserve model quality at lower bit widths.

quantization/llm/research/efficiency

papersSEP 10 04:00 UTC

When Does Low-Bit Quantization Preserve the Decisions of Vector Search?

A new arXiv study investigates why low-bit quantization delivers high recall on some embedding types while failing sharply on others, a difference that average distortion and global rank correlation cannot explain. The authors analyze quantized vector search at the level of individual comparisons to identify when compressed indexes still reproduce full-precision retrieval decisions.

quantization/vector-search/retrieval/embeddings