Multilingual communication

Models 9

• Gen 7
  Apertus v1.1 4B

  By Swiss AI Initiative

  Apertus-v1.1-4B is a 4B parameter open-source base language model created via pre-training distillation from the Apertus-8B teacher model. It uses a dense transformer with grouped-query attention and xIELU activations, natively supports 1,811 languages, and is trained on 1.7T high-quality tokens. Designed for constrained hardware and further fine-tuning.

  📚Large Language Models🔍Research🌐Multilingual communication🧠Model training
  NewText

  Released 9d ago
• Gen 3
  MOSS TTS Nano

  By OpenMOSS

  MOSS-TTS-Nano is an open-source multilingual tiny speech generation model from MOSI.AI and OpenMOSS. With only 0.1B parameters, it is built for real-time TTS, can run directly on CPU without a GPU, and keeps deployment simple enough for local demos, web serving, and lightweight product integration.

  🔊Text to speech🗣️Voice cloning🌐Multilingual communication
  NewMultimodal

  Released 2mo ago
• Gen 7
  Harrier OSS v1

  By Microsoft

  Harrier is Microsoft’s new open-source text embedding model series for retrieval, grounding, and agentic systems. It is designed for the “agentic web,” supports over 100 languages, uses a 32K context window, and was announced as ranking first on multilingual MTEB-v2 as of 06-04-2026.

  🔍Information retrieval📂Content categorization🌐Multilingual communication🔍Conceptual search
  Text

  Released 3mo ago
• Gen 3
  OmniVoice

  By Xiaomi

  OmniVoice is a multilingual zero-shot text-to-speech model built for voice cloning, voice design, and general speech synthesis at massive language scale. It supports more than 600 languages, uses a diffusion language model-style architecture, and is positioned for high-quality speech generation with fast inference.

  🔊Text to speech🎤Voice changing🗣️Voice cloning🌐Multilingual communication
  Multimodal

  Released 3mo ago
• Gen 3
  TADA 3B ML

  By Hume AI

  TADA-3B-ml is a multilingual TADA checkpoint built for fast, reliable speech generation using the same 1-to-1 text-acoustic alignment framework.

  🔊Text to speech🗣️Voice cloning🗣️Speech to speech🎙️Voiceovers🌐Multilingual communication
  Multimodal

  Released 3mo ago
• Gen 7
  BGE Reranker v2 minicpm layerwise

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE Reranker v2 minicpm layerwise is BAAI’s layer-selectable multilingual reranker in the v2 family. It is built to let users choose output layers for accelerated inference, trading some depth for speed while keeping strong multilingual reranking performance.

  🔍Information retrieval🌐Multilingual communication
  Text

  Released 2y ago
• Gen 7
  BGE Reranker v2 gemma

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE Reranker v2 gemma is BAAI’s larger multilingual reranker in the v2 family. It is designed for stronger multilingual reranking performance, with the docs highlighting good English proficiency alongside broad multilingual capability.

  🔍Information retrieval🌐Multilingual communication
  Text

  Released 2y ago
• Gen 7
  BGE Reranker v2 m3

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE Reranker v2 m3 is BAAI’s lightweight multilingual reranker in the v2 family. It is built for fast inference and easier deployment while keeping strong multilingual reranking capability, making it the efficiency-oriented option in the v2 lineup.

  🔍Information retrieval🌐Multilingual communication
  Text

  Released 2y ago
• Gen 7
  BGE M3

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE M3 is BAAI’s multilingual retrieval embedding model built around three strengths: multi-functionality, multi-linguality, and multi-granularity. It supports dense retrieval, sparse retrieval, and multi-vector retrieval in one model, works across 100+ languages, and handles inputs up to 8192 tokens.

  🔍Information retrieval🌐Multilingual communication🔍Conceptual search
  Text

  Released 2y ago