AI Model2026
Provides GGUF-format fine-tuned Qwen3.6-27B weights optimized for consumer hardware, offering NEO IMATRIX and MTP quant variants, vision support, 256k native context, and uncensored 'heretic' traces with published benchmark improvements over the base model.
Embodied AI2026
Generates robot manipulation actions from visual observations and text instructions using a 1.5B vision-language-action model. Uses streaming context and visual-token compression to cut per-step compute, runs a unified policy across tasks, and is open-sourced on Hugging Face under Apache-2.0.
Embodied AI2026
Predicts eight future [x,y,yaw] waypoints for language-conditioned embodied person-following using fused DINOv3 and SigLIP visual features; trained with quality-driven, DAgger-style self-evolving data and optimized for on-device inference (~5+ FPS, ~180 ms).
AI Dataset2026
Provides verified, model-attested end-to-end agent coding and debugging trajectories (JSONL). Each whole-session trace was produced by moonshotai/kimi-k3 on the pi/openrouter runtime, passed acceptance tests and independent model screening — useful for SFT, distillation, and analyzing tool-use behavior.
AI Dataset2026
Provides intermediate pretraining checkpoints for the Aether-7B-5Attn base model to enable reproducible training-dynamics research. Includes three raw checkpoints (110k, 115k, 162k steps) packaged with model.safetensors, config, and tokenizer; uses a custom aether_v2_7way architecture requiring the aether_pkg loader.
AI Dataset2026
Provides newline-delimited JSON agent session traces (5 files) generated with Teich for moonshotai/kimi-k3, including recovered and embedded tool-schema snapshots so traces remain training-ready even when tools weren't invoked; includes guidance for Teich data preparation and conversion.
AI Model2026
GGUF-quantized releases (NEO IMATRIX + MTP) of a multi-stage fine-tuned, uncensored Qwen3.5-9B model with vision enabled and a native 256k context window—optimized for instruction following, reasoning and image-text-to-text workflows; released under Apache-2.0.
AI Dataset2026
Provides 2,000 hours of synchronized, high‑fidelity robot‑free bimanual manipulation demonstrations with multi‑view video, calibrated end‑effector trajectories, gripper states, and language annotations. Curated from a 20,000+ hour corpus; features 6 camera views, ~3 mm pose accuracy, <40 µs cross‑sensor sync, and LeRobot v3‑style Parquet+MP4 export under CC BY 4.0.
AI Dataset2026
Provides under-1K JSON agent-trace records documenting model refusal responses and forensic metadata — useful for evaluating refusal-detection, audit pipelines, and safety analysis; small size limits large-scale statistical studies.
AI Model2026
Adds vision to GLM-5.2 by attaching a MoonViT encoder and a trained 49.5M-parameter PatchMerger projector to enable image→text multimodal reasoning; text and vision backbones are frozen, uses NVFP4 quantized weights and targets Blackwell B200 GPUs.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video and action trajectories—conditioned on text, images, video and robot/vehicle action inputs. An edge-sized (4B) Mixture‑of‑Transformers omni-model optimized for single‑GPU inference and Physical AI tasks (image→video, action prediction, robot policy).
AI Dataset2026
A multiple-choice benchmark for evaluating language-model arithmetic: 1,000 continuation-style elementary word problems (4 choices, balanced labels) organized by topic, grade band, and difficulty. Designed for base-model continuation log-likelihood scoring; released under Apache-2.0.