AI Model2026
Provides a 27B-class Qwen3.6-derived language model in GGUF with end-to-end ternary weights (Q2_0_g128), reducing deployed footprint to ~7.2 GB while retaining ~95% of FP16 reasoning ability and enabling on-device 262K-token context inference.
AI Video Papers2026
Converts an academic paper into reusable extracted assets and then produces editable poster, synchronized talk video, and bilingual blog via modular generator skills. Key differentiator: a single Paper2Assets extractor shared by three editable generators plus an interactive Paper2Reel viewer that links slides, video, captions and blog while preserving factual consistency and round-tripable PPT/DOCX output.
AI Dataset2026
Provides re-annotated academic video instruction data for captioning, video QA, and fine-grained motion understanding; rewrites short answers and concise captions into evidence-grounded, instruction-following responses and supplies JSONL annotation files (original videos not included).
AI Agent Papers2026
Trains cross-platform GUI agents by combining a Uni-GUI cross-platform dataset with platform-conditioned multi-teacher on-policy distillation, enabling a shared policy to adapt to new platforms while retaining platform-specific behaviors; suitable for research on continual GUI agent learning and cross-platform adaptation.
AI Model2026
Fine-tuned variant of Qwen3.6-27B that cuts internal reasoning (‘thinking’) token usage by roughly 46% on average while preserving benchmark accuracy and safety behavior. Targets lower latency and inference cost; ships on Hugging Face with GGUF quantizations for local use.
AI Video Papers2026
Provides a reflexive agentic framework for long-horizon video understanding that replaces costly iterative reasoning with dual contextual states: a consolidated global multimodal script and parametric latent states for fast retrieval and response, improving speed and memory efficiency.
AI Model2026
27B multimodal LLM post-trained to prioritize agentic, weight-scaled reasoning over 64K-token contexts. Built on Qwen3.6-27B and released with BF16 weights plus several GGUF quants; aimed at coding, long-document reasoning, tool use and multimodal inspection.
AI Video Papers2026
Autoregressively synthesizes long-horizon, playable video worlds conditioned on current state and user actions for real-time interaction. Ships as an open-source, full-stack framework covering data preparation, model architectures, training, inference acceleration, and deployment for interactive generative worlds.
AI Model2026
Instruction-driven LoRA fine‑tune for identity‑preserving image edits: give an image plus a plain‑language instruction and it edits pose, outfit, objects or scene while keeping unasked content and subject likeness. Requires the ComfyUI‑Krea2Edit node pack; distributed under the Krea 2 Community License.
Expresses diverse computer-vision tasks as instruction-driven text, image, or mixed generation from a single unified multimodal model, producing outputs for detection, segmentation, depth, pose, OCR and more. Trained on a converted SenseNova‑Vision instruction–response corpus and requires no task-specific prediction heads.
AI Video2026
Generates image-to-video world-model outputs using a distilled 14B causal model optimized for chunked, KV-cached inference across long-horizon interactive scenes; offers a real-time 'causal-fast' variant capable of driving near‑real‑time video streams and an agentic harness for action-driven scene synthesis (CC BY‑NC‑SA).
Performs native structural reasoning for proteins, small molecules and inorganic crystals by tokenizing coordinates, topologies and periodic connectivities into a unified structure-aware vocabulary. Treats structural tokens as addressable evidence to produce interpretable prediction traces and improves accuracy across biology, chemistry and materials benchmarks.