Synthesizes 234K self-contained, high-difficulty scientific reasoning QA pairs by distilling research papers into compact 'reasoning skeletons'. Emphasizes mechanistic reasoning, hypothesis falsification, quantitative derivation and boundary calibration; built for SFT and reasoning evaluation.
Provides image-level tag correction instructions for a Danbooru anime-image tagging corpus, listing per-post tags to add or remove. Contains 1.74M normalized correction rows (snapshot 2026-08-30); it's a corrections manifest (no images) intended to be applied to existing metadata.
Provides 997 chain-of-thought cybersecurity reasoning records distilled from the Kimi K3 model, each with an explicit <think> trace and a technical resolution or structured tool invocation. Includes verified tool-call objects, diffs, cross-domain coverage, and token-level metadata for fine-tuning and evaluating reasoning models.
Provides a human-verified benchmark of 1,927 heterogeneous articulated 3D objects with part-level articulation semantics and intrinsic physical-property annotations for evaluating physical grounding and simulation readiness. Includes URDF assemblies, aligned point clouds, per-part JSON annotations, and a curated evaluation protocol; licensed CC BY-NC 4.0 (non-commercial).
Replaces a character in a video using a single repainted frame from the same clip and propagates that edit across the shot while preserving motion, camera and lighting; requires no pose estimator, segmentation, face tracker or text prompt. Key facts: a 33.1B MiniMax-H3 finetune, DMD-distilled to three forward passes, 124 frames in ~26s on one B200 GPU.
An experimental multimodal model that adds visual understanding to DeepSeek-V4-Flash: accepts text+image inputs and returns text analyses. Improves vision-dependent agent workflows while maintaining comparable text-only performance; released under an MIT license on Hugging Face.
Provides 23,625 semi-structured smart-contract audit findings (title, description, PoC, recommendation, normalized severity) for defensive-security research; requires cleaning, deduplication, and PoC filtering before model training.
Provides 315,000 pairwise human-preference votes comparing 15 English TTS models over 300 operational prompts, with 4,500 high‑quality audio renders and structured vote/pair/prompt records for training or evaluating preference/reward models. Metadata under CC-BY-4.0; audio use governed by model providers' terms.
Provides an unattended text-to-video-and-audio streaming toolkit built around FastH3 (a 4-step distillation of MiniMax-H3): generation/retime/HTTP push scripts, a 221-scene prompt library, checkpoint conversion and ComfyUI workflows to run a continuous local stream.
Sparse MoE causal LLM that uses Mixture-of-Value Attention (MoVA) to store 36B parameters while activating ~4B per token; supports a native 524,288-token context and is released with final checkpoints, training data, and training code under open license.
A TURBO multi-stage fine-tune of Qwen3.8‑27B that shortens internal “thinking” token blocks and raises ARC benchmarks (8‑bit ARC‑C ≈735, ARC‑E ≈882). It ships GGUF quants (regular and MTP, Neo‑Imatrix), vision support and 256k context for local multimodal inference on consumer GPUs.
NVFP4-quantized checkpoint of Qwen3.8-Flash-Next for GPU-optimized multimodal autoregressive inference — routed MoE experts in W4A4 NVFP4 while attention/ancillary layers remain BF16; ~2.7× smaller than the BF16 source and supports very long contexts.