Agentic coding behaviour is one of the hardest-to-capture model characteristics because it combines multi-step reasoning, tool use, and language-specific implementation details. This preview exposes raw V4‑Pro outputs so researchers and engineers can study how an
Tachibana4-PREVIEW
Early-preview (≈1.2k rows) dataset of agentic coding prompts and unedited model responses generated by DeepSeek‑V4‑Pro, covering real-world programming tasks across many languages. Intended for research, filtering, and model evaluation rather than production training without review.
Introduction
Information
- Websitehuggingface.co
- Authorssequelbox
- Published date2026/04/30
Categories
More Items
A 12K-question multiple-choice benchmark that raises difficulty by expanding choices to ten and adding reasoning-focused problems. Designed for robust LLM evaluation with cross-domain coverage, expert curation, and emphasis on chain-of-thought performance.
A multimodal supervised fine‑tuning corpus for training MLLMs: 44.3M samples (100.3M QA pairs) over 19.7M image groups, annotated with short/long chain‑of‑thought and produced by rerunning the HoneyPipe curation pipeline with multi‑model rollouts and verifications.
A fine-grained, domain-labeled web corpus for NLP and LLM research that decomposes a deduplicated web crawl into 67 domains via iterative URL/content labeling and coarse→fine recall; includes per-domain statistics, validation subsets, and domain-benchmark analysis tools.