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Curated multimodal training corpus for spatial intelligence: ~8.16M QA-style samples paired with ~2.72M unique images (≈1.1 TB). Provides JSONL annotations, a 1,000-sample preview, and 52 independent image archives — used to train SenseNova-SI models.
Distills DeepSeek‑V4's multi-step structured reasoning into a Qwen3.5‑9B model for fast image-text-to-text reasoning and agentic tool workflows. Trades larger teacher size for inference efficiency and improved procedural reasoning — good for low-latency research, evaluation, and agent integration.
DavidAU/Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF
A 40B GGUF-quantized Qwen3.6 variant fine-tuned with Claude 4.6 Opus and Deckard/Heretic datasets for multimodal image-text-to-text tasks. Offers 256K context, custom NEO-CODE Di-IMatrix quants for long conversations and coding, optimized for local inference and creative/coding use cases; safety alignment removed.
Instruction‑tuning dataset of 8,706 Claude Opus 4.6/4.7–generated examples where each assistant turn begins with a synthetic <think> block to emulate chain‑of‑thought. Provided as four splits (full/instruct/roleplay/code), ~17M tokens total, Apache‑2.0, not manually reviewed.
A prompt-only mixture of ~478k prompts designed to support antidoom-style generation and preference-data pipelines for reducing model repetition (doom loops). Prompts are stripped of answers and labels and sourced from many public datasets so it’s usable for FTPO/adapter generation but not for supervised QA evaluation.
Provides 19,331 multi-turn ChatML Hermes reasoning traces produced by DeepSeek V4 Pro for LoRA fine-tuning of agent-style models; includes VRAM-tiered variants, train/valid/test splits, and dense tool-calling annotations in Parquet format.
Large-scale synthetic video dataset of physically simulated multi-object interaction scenes for training and evaluating models on physical reasoning, depth and optical-flow estimation, instance segmentation, and physics-grounded captioning. Provides RGB + lossless depth, per-frame instance masks, per-object physics annotations (NPZ), VLM-grounded captions, and USD scene files — useful for world-model and simulation-to-real work; commercial use permitted.
A reasoning-enhanced Mixture-of-Experts (MoE) LLM fine-tuned for multimodal image-text-to-text tasks and long-context reasoning; built on Qwen3.6-35B-A3B with LoRA and released as an experimental GGUF community model.
Trains reusable natural-language 'skills' for frozen LLM agents by optimizing the skill document in text-space — using trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts. Multi-backend, zero inference-time cost at deployment, designed for iterative, validation-led skill improvement.
Provides tick-aligned Counter-Strike 2 player POV video clips with per-tick inputs and world-state sidecars — near-lossless 1280×720@32fps video, per-player stereo audio, and parquet indexes for event/kill/round filtering; suited for RL, video classification and clip mining.
Contains 4,006 newline-delimited JSONL agent-session traces recording assistant responses and tool calls from deepseek/deepseek-v4-pro — includes a training-ready tools schema snapshot and helpers for conversion to SFT/distillation workflows.
Generates high-quality Japanese speech from text with zero-shot voice cloning and emoji-based style controls; uses a flow-matching diffusion transformer over DACVAE continuous latents, includes a duration predictor and integrated SilentCipher watermarking. Japanese-only.