AI models directory for capability-aware discovery

T5-small is a compact, multilingual encoder-decoder transformer model developed by Google and hosted on Hugging Face. Licensed under Apache 2.0, it supports text-to-text generation tasks such as translation, summarization, and question answering across multiple languages including English, French, German, and Romanian. It is optimized for low-latency inference and efficient deployment on various hardware backends.

BGE Reranker v2 M3 is a cross-encoder reranking model by BAAI, built on XLM-RoBERTa, supporting 100+ languages. It outputs relevance scores for query-document pairs, achieving state-of-the-art results on multilingual retrieval benchmarks. Licensed under Apache 2.0, it has over 16 million downloads and is deployable via Hugging Face, Sentence-Transformers, and Text Embeddings Inference.

Qwen2.5 is a comprehensive series of large language models developed by Alibaba Group's Tongyi Lab, available via Ollama for local deployment. It features parameter sizes ranging from 0.5B to 72B, supporting contexts up to 128K tokens and multilingual capabilities across 29+ languages. The series is distinguished by significant enhancements in coding, mathematics, instruction following, and structured output generation compared to its predecessor, Qwen2.

Qwen2.5 is a family of large language models developed by Alibaba, pretrained on up to 18 trillion tokens with multilingual support and a context window of up to 128K tokens. Available in sizes from 0.5B to 72B parameters, it is optimized for local deployment via Ollama.

XLM-RoBERTa (XLM-R) is a multilingual transformer model introduced by Facebook AI in 2020, pre-trained on 2.5TB of CommonCrawl data covering 100 languages. It uses a masked language modeling objective and achieves state-of-the-art results on cross-lingual benchmarks like XNLI and MLQA. The base version has 278M parameters and is available in PyTorch, TensorFlow, JAX, and ONNX formats via Hugging Face.

The `paraphrase-multilingual-MiniLM-L12-v2` model is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space for tasks like clustering, semantic search, and paraphrase identification. It is based on Microsoft's MiniLM-L12-H384-uncased architecture and fine-tuned on a large multilingual paraphrase corpus, supporting over 50 languages. The model is released under Apache 2.0 and is available in PyTorch, TensorFlow, ONNX, OpenVINO, and SafeTensors formats.