AI Dataset2026
Synthetic Korean-language persona dataset for training and evaluating conversational and generative models — 1M records (≈7M persona entries) with 26 fields aligned to South Korea’s demographic distributions. Built with NeMo Data Designer and released under CC BY 4.0.
AI Model2026
Instruction-tuned 13B LLM post-trained on 260B tokens of pre-1931 English and finetuned with online DPO (LLM-as-judge) to improve instruction-following; suited for period-style English generation and etiquette/letter-writing formats, but not optimized for contemporary factual updates.
AI Dataset2026
Benchmark dataset for evaluating clinician-facing chat assistants: physician-authored conversations plus rubric items, use-case and difficulty labels, specialty metadata, and a built-in canary to reduce benchmark contamination. Hosted on Hugging Face under an MIT license.
AI Dataset2026
Provides 150,000 synthetic Vietnamese patient personas to condition clinical text generation. Each persona bundles demographics, socioeconomic context, health and behavior fields, and prompt-ready narratives; intended for research and simulation, not clinical decision-making.
AI Dataset2026
Benchmarks ASR on long-form English call-center conversations with wide accent coverage; 128.6 hours across 14 accent groups and 16 service domains, designed for segmentation-sensitive evaluation and intended for evaluation/analysis (CC BY‑SA 4.0).
AI Model2026
A Qwen-3.6 27B model variant optimized for DFlash (speculative decoding) to reduce generation latency and increase throughput. Focuses on faster inference on serving stacks and is suitable for text-generation endpoints where lower latency and resource efficiency matter.
AI Dataset2026
Supervised fine-tuning dataset of 7,716 reasoning-focused Q&A examples distilled from the DeepSeek‑V4‑Flash teacher; provided as a cleaned JSONL train split for distillation and SFT experiments.
AI Dataset2026
1,000 JSONL samples containing full chain-of-thought reasoning traces and final answers produced by DeepSeek‑V4‑Pro for use in student-model distillation and quality checks. Prompts sampled from Jackrong/GLM-5.1-Reasoning-1M-Cleaned; Apache‑2.0 licensed.
AI Dataset2026
Collects real-world developer–AI coding sessions with full transcripts, tool calls, agent thinking traces, Git commits, and agent vs. human code attribution. Packaged as Parquet tables (conversations, sessions, commits, checkpoints, repositories) for analysis of agent behavior and human–AI collaboration.
AI Dataset2026
Contains full chain-of-thought traces and final answers generated by DeepSeek-V4-Pro for use as distillation supervision. Key features: full CoT exposure, ~1,000 mixed-domain samples (JSONL/Parquet), Apache-2.0 license — suitable for training student models but watch for source contamination.
AI Dataset2026
Cleaned dataset of reasoning-distillation examples derived from Claude Opus 4.7 outputs — 4,807 retained JSON chat rows after removing simulated-thinking, duplicates, and missing fields. Packaged for model distillation and reasoning evaluation; Apache-2.0 packaging with upstream Anthropic usage constraints.
AI Dataset2026
Training dataset for byte-level language identification across 334 languages with ~2.48M paragraph samples (primarily Wikipedia and open-licensed corpora). Curated to reduce multilingual contamination, boost low-resource coverage, target frequent confusions, and preserve per-row license metadata for attribution.