AI Model2026
Reasoning-enhanced 27B dense LLM fine-tuned from Qwen3.6-27B and released in GGUF format for image-text-to-text and long-context reasoning. Augmented with Trace Inversion reconstructed chains, three-stage SFT curriculum and MTP/vision support; community research release.
AI Model2026
W4A4-quantized build of a 25B-parameter multimodal LLM that produces text from image+text inputs and supports conversational tool use. Trades very small quality differences for much lower GPU memory and latency so inference can run on smaller accelerators (vLLM support).
AI Audio2026
Converts long-form multi-speaker audio/video into a compact, speaker-aware transcript with timestamps and anonymous speaker labels in one pass. Combines ASR and diarization in a single model, supports custom prompts/hotwords, and targets meetings, podcasts, interviews and long recordings.
AI Model2026
Generates temporally coherent MP4 videos from a single input image plus text instructions, with configurable resolution, frame count, and optional AAC audio. Optimized for NVIDIA GPU stacks and integrates with vLLM‑Omni and Hugging Face Diffusers for production inference and research workflows.
AI Video2026
Generates audio-driven avatar videos from text, images, or audio inputs with production-grade stability (accurate lip sync, identity consistency) and an 8-step distillation inference mode for faster serving; suitable for broadcasting, virtual hosts, animation, and multi-person scenarios.
AI Model2026
Instruction-tuned, unified Gemma 4 12B multimodal model that accepts text, image and audio inputs and generates text outputs locally. Encoder-free design reduces multimodal latency and fits on consumer devices while offering long-context support and native thinking/system-prompt features.
AI Model2026
A 12B unified, encoder-free multimodal model that directly ingests text, images and audio and returns text; supports very long contexts (up to 256K tokens), native function-calling/thinking modes, and small-model deployment for local or on-device use.
AI Model2026
Processes images and text to produce structured, reasoning-rich text outputs for high-throughput agentic workflows. Sparse MoE design (198B total, ~11B active per token), 256k context window and selectable reasoning levels—optimized for single-pass parsing, verification, and multi-step automation.
AI Model2026
Performs hour-scale video understanding and fine-grained temporal localization while exposing agent-style multimodal tool/code/search abilities. Built on a sparse-attention long-context architecture (DSA) and a specialized inference stack—best used in GPU-backed research or production evaluation.
AI Model2026
Performs fast, high-quality vision–language grounding: given an image plus a natural-language prompt it returns bounding boxes or points for referred objects. Uses Parallel Box Decoding for parallel coordinate prediction (higher throughput) and targets research/non-commercial use.
AI Dataset2026
Benchmark for evaluating vision–language models on measurement-grounded inputs vs. RGB, emphasizing low-light, HDR, and visibility-sensitive evidence recovery. Contains 2,183 paired test examples with local image assets for controlled RAW↔RGB comparisons.
AI Model2026
Performs image-to-text document parsing and OCR for complex elements (tables, formulas, charts, seals), with multilingual support (en/zh). It uses region-aware data optimization and progressive post-training to improve weak-region supervision and is plug-and-play compatible with PaddleOCR-VL-1.5.