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Contains 40,000 teacher-generated reasoning traces distilled from the Qwen3.8-27B model for supervised fine-tuning and analysis. Covers code, math, science and logic; each example pairs a <think> chain-of-thought with a final response and is distributed in JSONL/Parquet for SFT workflows.
Autonomously executes diverse biomedical research tasks by combining LLM reasoning, retrieval-augmented planning, and code-based execution. Includes a web UI and Gradio demo, a curated Know‑How library, MCP integration, and a biology-tailored reasoning model (Biomni‑R0).
Provides a deterministic context and knowledge-graph layer under LLMs and vector stores to record auditable decisions, provenance, and explainable rule-based reasoning. Supports polyglot graph storage, W3C PROV-O export, SHACL governance, and self-hosted enterprise connectors.
Provides a machine-readable collection of 5,426 open and historically significant mathematical problems with LaTeX statements, structured metadata and curated per-problem AI-assisted research notes. Includes difficulty labels, canonical problem sets (Millennium, Hilbert, Erdős) and files optimized for benchmarking math reasoning.
Runs the Bonsai family of quantized LLMs locally (including vision-capable 27B): provides scripts and demo UIs to run 1-bit and ternary Bonsai models on macOS (Metal), Linux/Windows (CUDA/Vulkan/ROCm), or CPU, with long context, tool-calling and an optional Open WebUI agent demo.
Unifies multimodal understanding, reasoning, and image generation in a single end-to-end architecture using the NEO-unify paradigm. Models pixels and words jointly without a separate visual encoder, and provides interleaved image–text generation, infographic editing, and GGUF/low‑VRAM inference options.
A collection of biology-focused 'mystery' tasks for benchmarking model performance on biomedical reasoning, evidence synthesis, and problem solving; curated by Anthropic and hosted on Hugging Face, designed for granular evaluation of scientific decision-making.
Provides 545,431 math problems with model-generated solution traces (chain-of-thought and Python tool-integrated reasoning) verified against reference answers for training and evaluating LLM mathematical reasoning. Parquet-format dataset; DeepSeek‑V4‑Pro generated traces and mixed CC BY / CC BY‑SA licensing.
Spawns parallel, isolated LLM reasoning frames, then scores, clusters and prunes ideas to avoid premature convergence. Packaged as a reusable Claude/Codex agent skill with CLI and TypeScript APIs for ideation, design decisions and fuzzy debugging.
Provides ~3.8M chain-of-thought training examples (~3B tokens) that use a fixed 'Inventory / Interaction / Execution' scaffold to improve concise, reliable reasoning in small LLMs; topic-labeled, deduped, includes real tool-execution traces, CC-BY-4.0.
Provides 16M+ instruction–response samples and ~81 GB (7,090 compressed GitHub repos) distilled from 68 open-source sources, organized into 8 categories for SFT, coding agents and reasoning research. Model-generated content; released as a curated MIT-licensed collection.
Runs a full 27B-class language model using end-to-end binary (1.125-bit) weights, cutting FP16 size to ~3.9 GB. Key features: 262k-token context, custom 1-bit kernels for Apple MLX and CUDA, and an optional DSpark drafter for faster decoding. Best when memory footprint matters; trades some FP16 accuracy for on-device feasibility.