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.
[next-mdx-remote-client] error compiling MDX: Unexpected character `~` (U+007E) before name, expected a character that can start a name, such as a letter, `$`, or `_` More information: https://mdxjs.com/docs/troubleshooting-mdx
Provides manually curated Japanese instruction pairs (questions and safe reference answers) for improving LLM output safety, covering broad harm categories and regionally sensitive cases. Includes English meta-tags and standard splits for benchmarking and fine-tuning.
A 16 GB, 507-file PhD‑level cybersecurity knowledge base for training and evaluating security-focused LLMs and automation. Covers offensive/defensive/forensics/cloud/iot and AI-security across 30+ domains with real-world labs and framework mappings.
Structured dataset for training and evaluating LLM agentic behavior: function-calling conversations, JSON-mode structured outputs, and extraction samples for teaching models to generate tool calls and strict structured responses. Includes single-turn and multi-turn scenarios across several configs.
