SAVRN
Search Contact SAVRN

Organization

KaLM-Embedding

KaLM-Embedding

Building the foundation for better search with embeddings, rerankers, RAG infrastructure, and small language models.

Models in Library3
Datasets in Library0
Models on Hugging Face13
Followers54

Models

KaLM-Reranker-V1-R2 is a substantially improved checkpoint release of KaLM-Reranker-V1. It keeps the same fast-but-not-late-interaction (FBNL) architecture and inference interface as the original release, while introducing a stronger multi-stage training recipe for three practical goals: 1. Stronger compression robustness. R2 supports Matryoshka embedding pooling (MEP) from 1x to 128x, extending the maximum validated compression ratio from 32x to 128x. Even at 128x compression, all three model sizes retain at least 92% of their average nDCG@10 at 2x compression on both BEIR and MIRACL. 2. Adjustable test-time compute scaling. A single KaLM-Reranker-V1-R2 checkpoint can trade compute for…

Open weights apache-2.0 786M parameters sentence-transformers

KaLM-Reranker-V1-R2 is a substantially improved checkpoint release of KaLM-Reranker-V1. It keeps the same fast-but-not-late-interaction (FBNL) architecture and inference interface as the original release, while introducing a stronger multi-stage training recipe for three practical goals: 1. Stronger compression robustness. R2 supports Matryoshka embedding pooling (MEP) from 1x to 128x, extending the maximum validated compression ratio from 32x to 128x. Even at 128x compression, all three model sizes retain at least 92% of their average nDCG@10 at 2x compression on both BEIR and MIRACL. 2. Adjustable test-time compute scaling. A single KaLM-Reranker-V1-R2 checkpoint can trade compute for…

Open weights apache-2.0 2.1B parameters sentence-transformers

KaLM-Reranker-V1-R2 is a substantially improved checkpoint release of KaLM-Reranker-V1. It keeps the same fast-but-not-late-interaction (FBNL) architecture and inference interface as the original release, while introducing a stronger multi-stage training recipe for three practical goals: 1. Stronger compression robustness. R2 supports Matryoshka embedding pooling (MEP) from 1x to 128x, extending the maximum validated compression ratio from 32x to 128x. Even at 128x compression, all three model sizes retain at least 92% of their average nDCG@10 at 2x compression on both BEIR and MIRACL. 2. Adjustable test-time compute scaling. A single KaLM-Reranker-V1-R2 checkpoint can trade compute for…

Open weights apache-2.0 7.5B parameters sentence-transformers