Model Library
Browse and deploy state-of-the-art AI models through the DEVUP Gateway.
Browse and deploy state-of-the-art AI models through the DEVUP Gateway.
Browse and deploy state-of-the-art AI models through the DEVUP Gateway.
all-mpnet-base-v2 is an open-source sentence embedding model developed by Sentence Transformers, built upon Microsoft's 109-million parameter MPNet (Masked and Permuted Pre-training) architecture. By combining the strengths of masked language modeling (BERT) and permuted language modeling (XLNet), it maps text into a dense 768-dimensional vector space. Fine-tuned on over 1 billion sentence pairs using contrastive learning, it long served as the primary quality gold standard among Sentence Transformers for semantic similarity, classification, and retrieval tasks.
all-MiniLM-L6-v2 is an ultra-lightweight, high-speed sentence embedding model developed by Sentence Transformers. Built on the 22.7-million parameter nreimers/MiniLM-L6-H384-uncased architecture (a 6-layer distilled version of BERT), it maps text into a compact 384-dimensional dense vector space. Trained on over 1 billion sentence pairs using self-supervised contrastive learning, it serves as the industry standard benchmark for edge deployments, in-browser inference, and real-time semantic search with minimal latency.
all-MiniLM-L12-v2 is a widely used, highly efficient sentence embedding model developed by Sentence Transformers. Based on the 33.4-million parameter microsoft/MiniLM-L12-H384-uncased architecture, it maps sentences and short paragraphs into a dense 384-dimensional vector space. Fine-tuned on over 1 billion sentence pairs using self-supervised contrastive learning, it offers an excellent balance between retrieval speed, memory footprint, and semantic accuracy, making it a foundational baseline for lightweight semantic search and clustering applications.
multilingual-e5-large-instruct is an open-source, instruction-tuned multilingual text embedding model developed by Microsoft Research. Built upon the 568-million parameter XLM-RoBERTa-large architecture, it supports over 100 languages and maps text into a 1024-dimensional dense vector space. Unlike previous E5 models that relied on rigid static prefixes, this model utilizes flexible, task-specific natural language instructions for queries while keeping target documents prefix-free, delivering superior custom retrieval accuracy and cross-lingual performance for global enterprise RAG systems.
multilingual-e5-large is an open-source, multilingual text embedding model developed by Microsoft Research. Built upon the 568-million parameter XLM-RoBERTa-large architecture, it supports over 100 languages and maps text into a 1024-dimensional dense vector space. Utilizing weakly-supervised contrastive pre-training and asymmetric prefix conditioning ("query: " and "passage: "), it delivers state-of-the-art cross-lingual retrieval and semantic search capabilities, making it a foundational model for global Retrieval-Augmented Generation (RAG) pipelines.
e5-large-v2 is an open-source text embedding model developed by Microsoft Research. Based on a 335-million parameter BERT-large encoder architecture, it maps text into a dense 1024-dimensional vector space using weakly-supervised contrastive pre-training followed by supervised fine-tuning. It utilizes asymmetric prefix conditioning ("query: " and "passage: ") to optimize dense retrieval and semantic search, delivering strong zero-shot retrieval accuracy across diverse domains with predictable inference latency.
e5-base-v2 is an open-source text embedding model developed by Microsoft Research. Based on a 109-million parameter BERT architecture, it is pre-trained using weakly-supervised contrastive learning. It generates 768-dimensional dense vectors and requires specific text prefixes ("query: " and "passage: ") to distinguish between search intents and document indexing, offering a strong balance between high retrieval accuracy and minimal computational cost for semantic search and RAG applications.
bge-m3-multi is an alternative designation for the bge-m3 embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI). Built on a 567-million parameter XLM-RoBERTa architecture, the "Multi" suffix emphasizes its core design pillars: Multi-Linguality (supporting over 100 languages), Multi-Functionality (simultaneous Dense, Sparse, and Multi-Vector ColBERT outputs), and Multi-Granularity (handling inputs up to 8,192 tokens). It serves as a unified, state-of-the-art engine for hybrid search and complex multilingual Retrieval-Augmented Generation (RAG) pipelines.
bge-m3 is a state-of-the-art multi-lingual, multi-functionality, and multi-granularity text embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI). Built on a 567-million parameter XLM-RoBERTa architecture, it supports over 100 languages and processes input sequences up to 8,192 tokens. It is uniquely engineered to output dense embeddings, lexical/sparse weights (similar to SPLADE), and multi-vector representations (ColBERT-style) simultaneously within a single forward pass, making it a foundational engine for versatile hybrid search and RAG systems.
bge-large-en-v1.5 is a high-performance open-source text embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI). Built on a 335-million parameter BERT-large encoder architecture, it maps text into a dense 1024-dimensional vector space optimized for semantic search, retrieval-augmented generation (RAG), and zero-shot ranking. Version 1.5 improves similarity score distribution and delivers top-tier performance across the Massive Text Embedding Benchmark (MTEB) while balancing embedding accuracy with practical inference latency.
bge-en-icl is a 7.11 billion parameter large language model-based embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI), built upon the Mistral-7B backbone. It introduces powerful in-context learning (ICL) capabilities to text embedding generation, allowing users to provide task-specific examples (few-shot prompts) alongside their queries. This eliminates the need for fine-tuning for new tasks while achieving state-of-the-art retrieval and semantic representation performance on benchmarks like MTEB and AIR-Bench.
bge-base-en-v1.5 is an open-source text embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI). Built upon a 109-million parameter BERT-base encoder architecture, it maps text into a dense 768-dimensional vector space optimized for semantic search, dense retrieval, and Retrieval-Augmented Generation (RAG) pipelines. Version 1.5 refines similarity score distributions and delivers top-tier performance on the Massive Text Embedding Benchmark (MTEB) while maintaining extremely low latency and hardware requirements.
