Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

Introducing RTEB: A New Standard for Retrieval Evaluation

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

Natural Language Query to Configuration for Retrieval Agents

Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and serving cost. Today, these pipelines are typically hand-tuned once per workload, leaving substantial per-query optimization untapped. We for...

Quanto: a PyTorch quantization backend for Optimum

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA