AI Dataset2026
Cleaned reasoning dataset of problem→thinking→solution triplets derived from Opus 4.6, provided in Parquet with ~2,160 cleaned rows (original 3,305). Filters remove empty/short/refusal/non‑substantive responses; hosted on Hugging Face under Apache‑2.0.
AI Dataset2026
Provides 1.06M web interaction trajectories (state, action, next_state) represented primarily as A11y trees for training browser world models and web agents. Covers diverse real‑web domains, English/Chinese pages, and long contexts (up to 30K tokens); residual PII and dynamic content may limit reproducibility.
AI Train2026
Provides a one-command CLI to fine-tune and post-train LLMs, with layer streaming that lets an 8B model be fine-tuned on a 4 GB laptop GPU. Auto-configures quantization, LoRA adapters, batching and evaluation gates, and supports export and serving workflows.
AI Model2026
A 26M-parameter LLM distilled for reliable function-call generation on tiny devices, with open weights, local finetuning tooling, and a web playground for on-device testing. Pretrained at scale then post-trained on a single-shot function-call dataset for tool integration.
AI Dataset2026
Paired brain MRI scans and radiology text annotations for multimodal vision–language research. Provides image-level labels and image–text pairs suited for VQA, classification, and image-to-text tasks; CC BY-NC-SA 4.0 and ~10K–100K samples — research/non-commercial use.
AI Video2026
Unmixes green‑screen pixels with a neural model to recover straight (unmultiplied) foreground color and a clean linear alpha for every pixel, preserving hair, motion blur and translucency. Produces VFX‑standard EXR outputs, supports optional AlphaHint generators (GVM/VideoMaMa) and Docker/consumer‑GPU optimizations.
AI Dataset2026
Provides 6,000 runnable, operator-level PyTorch tasks for training and evaluating CUDA kernel generation models; each sample includes executable code, operator descriptors, and provenance tags, with execution-driven filtering to ensure reproducibility and contamination control.
AI Model2026
An instruction‑tuned Gemma 4 E4B multimodal model on Hugging Face that accepts text, images and audio and generates text; notable for 128K long context support, built-in thinking mode, and an on‑device‑friendly E4B architecture under an Apache‑2.0 license.
AI Model2026
Identifies and surgically removes the internal activation directions that trigger refusal behavior in large language models, with one-click options on a HuggingFace Space or a local Python API. Combines multiple extraction methods (SVD, whitened SVD, sparse autoencoders), reversible steering, and analysis-informed verification to quantify capability and refusal trade-offs.
AI Model2026
Community fine-tuned multimodal Qwen3.5-9B using Claude 4.6 distilled data to change the model's 'thinking' behavior; offers an uncensored 'heretic' flavor with image-text-to-text I/O, benchmark comparisons, and deployment notes for inference frameworks.
AI Video2026
ComfyUI workflows that run LTX‑2.3 split models to produce text→video, image→video and audio→video pipelines. Uses extracted/split safetensor or GGUF files so models load more modularly; requires up‑to‑date ComfyUI, KJNodes and ComfyUI‑GGUF.