A new research paper published on arXiv investigates the effectiveness of post-training quantization (PTQ) techniques for text embedders. The study found that common heuristics for PTQ, such as protecting the embedding table and allocating bits based on module sensitivity, do not reliably transfer to retrieval embedders. The research quantizes models across various bit widths and group sizes, revealing that module sensitivity becomes family-dependent at lower bit widths and that a simple reconstruction proxy is less reliable for selecting tensors to protect. AI
IMPACT Findings may lead to revised strategies for efficient deployment of text embedders, potentially reducing computational costs and memory footprints.
RANK_REASON The cluster consists of a research paper detailing findings on post-training quantization techniques for text embedders.
- arXiv
- Hugging Face
- Int4
- INTS2
- NDCG@10
- NOTCH4
- Post Training Quantization Preprocessing Method of Convolutional Neural Network via Outlier Removal
- Text Embedders
- attention
- Embedding table
- Feed-forward blocks
- INT4/g16
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