News

New 0.6B-parameter listwise reranker that considers the query and all candidate documents in a single context window.

jina-reranker-v3 is a 0.6B parameter multilingual document reranker that introduces a novel last but not late interaction. Unlike late interaction models such as ColBERT that perform separate encoding followed by multi-vector matching, our approach conducts causal self-attention between query and documents within the same context window, enabling rich cross-document interactions before extracting contextual embeddings from the last token of each document. This compact architecture achieves state-of-the-art BEIR performance with 61.94 nDCG@10 while being significant smaller than generative listwise rerankers.

Embedding models aren't the most glamorous aspect of the AI industry, but image generators and chatbots couldn't exist without them.

We brought multimodal embeddings to llama.cpp and GGUF, and uncovered a few surprising issues along the way.

Code generation LLMs → code embeddings: 0.5B/1.5B models achieve SOTA performance across 25 code retrieval benchmarks.

jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages. It makes innovative use of an autoregressive backbone pre-trained on both text and code, generating embeddings via last-token pooling. We outline the training recipe and demonstrate state-of-the-art performance despite the relatively small size of the models, validating this approach to code embedding model construction.

Jina MCP streamlines agent development by connecting our APIs to any LLM, reducing custom code and improving reliability of the workflow.

4000 tokens/sec for a 3B-parameter embedding model on L4 GPU is probably as fast as you'll get with llama.cpp. Or is it?

Sharing what we saw and learned at SIGIR 2025, feat. CLIP-AdaM, RE-AdaptIR and evaluations for LLM-based retrieval systems.

Image resolution is crucial for embedding visually rich documents. Too small and models miss key details; too large and they can't connect the parts.