Tag
Explore by tags
Ensures LLM outputs match precise, schema-defined structures by enforcing Python types, Pydantic models, JSON schemas or grammars at generation time. Provider-agnostic integrations (OpenAI, transformers, vLLM, Ollama, etc.) and function-call style mapping reduce brittle post-processing and make structured generation reliable for production pipelines.
A 15,000+ English instruction–response corpus for fine-tuning and evaluating LLM instruction-following behavior. Contains human-authored prompts and answers across categories (closed/open QA, summarization, extraction, classification, brainstorming) and is released under CC BY-SA 3.0.
An open protocol that standardizes how LLM applications connect to external data sources, tools, and services via JSON-RPC — TypeScript-first schema with JSON Schema exports, SDKs, and centralized documentation to enable interoperable integrations.
Defines a vendor-neutral JSON/YAML semantic model specification and tooling to exchange metrics, dimensions, lineage and other business semantics across analytics, AI and BI platforms; includes a core spec, validators, converters (dbt, GoodData, Salesforce) and example models.
Transforms unstructured documents into strongly-typed Knowledge Abstracts with one CLI command, extracting entities and relations into graphs, hypergraphs, and spatio‑temporal structures. Includes 80+ templates, multiple RAG engines, local vLLM support, Obsidian export and an MCP server.
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.
Provides a command-line interface for AI agents to create, read, render, and modify Word/Excel/PowerPoint files headlessly. Includes a built-in high-fidelity HTML/PNG renderer, deterministic JSON APIs, resident mode and an MCP server for direct agent integration—suited for CI, containers, and automated document pipelines.
Provides 336,146 Turkish instruction-following chat examples (system→user→assistant) for supervised fine-tuning; single train split (no validation/test), reported MIT license, diverse tasks (rewrites, summarization, QA) and a uniform system prompt that may bias model behavior.
Structured, downloadable JSONL dataset of Seedance 2.0 video-generation prompts with matching MP4 previews and cover images; includes English/Chinese texts and standardized metadata (duration, resolution, safety) and is released under CC BY 4.0 for reuse.
Provides 40 public Kubernetes incident scenarios (SRE subset) with ground-truth root-cause entities and offline cluster snapshots in JSONL format; designed to evaluate agentic root-cause diagnosis on alerts, events, traces and topology.
Provides the gated, official OSWorld 2.0 Python task class files (task_*.py) required to run the benchmark; distributed via a Hugging Face gated dataset to reduce benchmark leakage. Download requires accepting gated access on Hugging Face.
Provides kanji-level evaluation data for Japanese TTS: disambiguated sentence contexts targeting 4,378 kanji-reading pairs (2,136 Jōyō kanji) with 13,095 native-speaker–verified sentences and katakana-marked ground-truth readings for kanji-level error metrics.