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.
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.
L3-8B-Lunaris-v1 is an 8-billion parameter large language model developed by Sao10K, based on the Meta Llama 3 architecture. It is a strategic 5-way model merge designed to serve as a versatile generalist and an immersive roleplaying assistant. Created as an improvement over the Stheno v3.2 series, Lunaris balances high creative output with enhanced logical consistency and reasoning capabilities, making it highly effective for both imaginative storytelling and structured conversational tasks.
L3.1-70B-Euryale-v2.2 is a 70-billion-parameter large language model fine-tuned by developer Sao10K on top of the Meta Llama 3.1 70B Instruct foundation. Specifically optimized for advanced creative writing, storytelling, and highly immersive interactive roleplay, it features a massive 131K context window for maintaining long-term memory and plot coherence. Trained on multi-turn conversational datasets synthesized from Claude 3.5 Sonnet, this open-weights model stands out for its deep spatial awareness, strict compliance with complex system prompts, and uncensored flexibility, making it a premier choice for generating rich, uninhibited narrative prose.
The gemma-4-E4B-it model is a lightweight, open-weights vision-language-audio model developed by Google DeepMind. It is optimized for high-performance, private, and offline on-device execution on smartphones, laptops, and IoT hardware. Featuring 4.5 billion effective parameters (8 billion total with embeddings) built on an innovative Per-Layer Embedding architecture, this instruction-tuned ("it") variant delivers state-of-the-art multimodal reasoning across text, images, and native audio. It also boasts a massive 128K context window and a built-in reasoning "thinking mode" to eliminate logic errors during local execution.
