A 30B-parameter, instruction-tuned language model built for long-context text generation, conversational agents, and tool-calling. It combines supervised fine-tuning and RL alignment, supports 131,072-token context, and is optimized for tasks like summarization, code, and RAG.
Desktop app for local voice cloning, real-time dictation, and end-to-end video dubbing using zero-shot TTS across 600+ languages; features multi-engine TTS/ASR, speaker diarization, vocal isolation, batch pipelines, and invisible audio watermarking — all run fully offline.
Provides 336,146 Turkish instruction-following chat examples (system→user→assistant) for supervised fine-tuning; single train split (no validation/test), reported MIT license, diverse tasks (rewrites, summarization, QA) and a uniform system prompt that may bias model behavior.
Provides deduplicated, sanitized Usenet posts (1980–2013) for language-model pretraining and linguistic research. Includes a ~103.1B-token full corpus (408M posts) with freely downloadable sample files; full corpus access requires a license and PII redaction was applied.
Clinical question-answering model for psychological support in obesity weight-management. Integrates UK Biobank population evidence to produce clinically interpretable, stigma-aware responses that help clinicians identify distress, prompt screening, and suggest appropriate referrals.
Produces 384‑dim multilingual (and code) embeddings with up to 32,768 token context, optimized for low‑latency production retrieval. Compact 97M model with ONNX/OpenVINO and vLLM/GGUF deployment options for edge and high‑throughput use.
Provides a lightweight assistant (draft) model for Gemma 4 E4B used in speculative-decoding pipelines — it predicts token drafts that the target model verifies in parallel, enabling up to ~2× decoding speedups while preserving identical final outputs. Useful for low-latency, multimodal assistant and on-device scenarios.
A lightweight 'drafter' assistant for Gemma 4 31B that generates speculative token drafts to enable up-to-2× decoding speedups while preserving final output quality; compatible with Hugging Face Transformers and any-to-any pipelines.
Acts as the assistant (drafter) checkpoint for Gemma 4 26B A4B on Hugging Face, used in Speculative Decoding to pre-draft tokens and speed up generation. Designed for long-context, multimodal workflows where lower latency and on-device or edge inference matter.
Generates expressive, scene-aware speech from XML-style prompts and supports zero-shot voice cloning from 10–20s references. Produces emotional acting, ambient SFX, multilingual output, and continuous long-form narration; requires large model weights and gated Gemma text-encoder access.
Training dataset for byte-level language identification across 334 languages with ~2.48M paragraph samples (primarily Wikipedia and open-licensed corpora). Curated to reduce multilingual contamination, boost low-resource coverage, target frequent confusions, and preserve per-row license metadata for attribution.
Provides a GGUF-quantized build of NVIDIA's Nemotron 3 Nano Omni 30B (Reasoning) for local inference — enables multimodal (video/audio/image/text) reasoning, transcription, and document understanding on compatible runtimes such as llama.cpp, Ollama, vLLM, and TensorRT-LLM.