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Open-weight frontier LLM for agentic reasoning and long-context analysis (up to 1M tokens). Uses a LatentMoE + Mamba-2 hybrid with Multi-Token Prediction and NVFP4 efficiency (550B total / 55B active). Suited for multilingual agents, RAG, and heavy tool-use workloads.
Converts text into expressive conversational speech across 100+ languages with zero-shot voice cloning and inline control tokens for emotion, style, prosody, pauses, and sound effects. Released under a research/non-commercial license; commercial use requires separate licensing.
Multilingual frontier LLM optimized for long-context reasoning and agentic workflows, combining a LatentMoE (Mamba-2 + MoE) hybrid architecture with Multi-Token Prediction and NVFP4 quantization; targeted for NVIDIA GPU deployments and governed by the OpenMDW-1.1 license.
Provides 600,000 synthetic Vietnamese persona texts (100,000 records, 6 personas per record) aligned to Vietnam's 2024 census and surveys for training and evaluating NLP / text-generation models; includes 21 demographic and persona fields, CC BY 4.0, single train split.
Trains LLMs with reinforcement learning using a surface chrF reward so models learn to extract and apply linguistic signals from rich context for translating completely unseen languages. Demonstrates better zero-shot translation than in-context learning or supervised fine-tuning, framing outcome-based RL as a meta-skill for language learning from context.
Multilingual, low-latency text-to-speech model for speech generation and zero-shot voice cloning. Uses an MoE backbone with ECAPA-TDNN speaker embeddings, supports audio prefixes, fine-grained prosody/emotion controls and 44.1kHz output; optimized for Linux + NVIDIA GPUs.
A post-trained Mixture-of-Experts multimodal LLM with ~397B total (≈17B active) and a 1,010,000-token context for image-text-to-text and conversational tasks. Integrates SwiReasoning to switch between latent and explicit reasoning; MIT-licensed and optimized for Portuguese/English research and on-prem inference.
Moves repository search into a dedicated exploration subagent that issues parallel read-only READ/GLOB/GREP calls and returns compact file:line citations. Trained (4B–30B) with SFT+RL, it reduces main-agent token use up to ~60% and raises end-to-end success by up to ~5.5%.
Provides a large language model optimized for long-horizon agentic tasks and end-to-end coding workflows — with a stable 1,000,000-token context, IndexShare sparse-attention and multi-level thinking-effort modes. MIT-licensed and designed for deployments that need sustained long-context reasoning and coding.
Provides 1.8M synthetic Belgian personas (1.2M records; 300k per language) in Dutch/French/German/English, grounded in Belgian census distributions to improve representativeness for LLM training and evaluation. Includes 23 persona and contextual fields, CC BY 4.0 license, produced with NeMo Data Designer.
Transcribes Arabic speech to text using a CohereLabs-trained ASR model compatible with the Hugging Face Transformers pipeline. Provides safetensors weights, endpoint compatibility and a DOI-tagged release; suitable for Arabic transcription workflows but may require adaptation for diverse dialects or noisy audio.
Provides over 1.1M hours of high-bandwidth, multichannel multilingual speech with segment- and word-level timestamps, English translations, and per-file metadata for ASR, TTS and audio-representation research. Preserves original 48kHz multichannel OPUS audio and is released under CC BY 3.0.