datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
twi-llm-reasoning-dataset-1k
This dataset is shared under CC BY-NC 4.0, which means you are free to use, share, and adapt it for non-commercial research and educational purposes with attribution. You can read the full license at https://creativecommons.org/licenses/by-nc/4.0/.
This dataset is made available because of Ghana NLP's volunteer driven research work. Please consider contributing to any of our projects on Github
Twi Reasoning Dataset
A Twi (Akan) translation of the Multilingual-Thinking… See the full description on the dataset page: https://huggingface.co/datasets/ghanaopenai/twi-llm-reasoning-dataset-1k.llm-medical-reasoning-steps-benchmark
LLM Medical Reasoning Steps Benchmark
This dataset contains 1,170 medical reasoning benchmark questions with final answers, reference reasoning steps, and reference key points.
Dataset Files
data/all.jsonl: all 1,170 examples.
data/mcq.jsonl: 592 multiple-choice examples.
data/oeq.jsonl: 578 open-ended examples.
No model prediction outputs are included in this release.
Schema
Each JSONL row has the following fields:
{
"id": "mcq_0001",
"task_type":… See the full description on the dataset page: https://huggingface.co/datasets/medreason/llm-medical-reasoning-steps-benchmark.ft-llm-2026-reasoning-sft
FT-LLM 2026 Reasoning SFT
A supervised fine-tuning (SFT) dataset of mathematical chain-of-thought traces used in Phase 2 of the COMPASS project to enhance the reasoning capability of a Japanese VLM's LLM backbone. Reasoning traces were distilled from a Qwen3-30B teacher over a broad pool of public math-reasoning datasets (GSM8K, MATH, SVAMP, AQuA-RAT, MathInstruct, MGSM-ja, Orca Math, NuminaMath-CoT, OpenR1-Math, Open Math Reasoning, and others). Each sample follows a structured XML… See the full description on the dataset page: https://huggingface.co/datasets/Yana/ft-llm-2026-reasoning-sft.ft-llm-2026-reasoning-dpo
FT-LLM 2026 Reasoning DPO
A Direct Preference Optimization (DPO) dataset of (prompt, chosen, rejected) triples used in Phase 2 of the COMPASS project to align a Japanese VLM's LLM backbone toward correct mathematical reasoning. The chosen responses are chain-of-thought traces distilled from a Qwen3-30B teacher in the structured <Problem>/<Thinking>/<Answer> XML format. The rejected responses are synthetically generated by corrupting the chosen responses under three strategies, mixed… See the full description on the dataset page: https://huggingface.co/datasets/Yana/ft-llm-2026-reasoning-dpo.High-Quality-Synthetic-Python-Dataset-with-Reasoning-Traces-Chain-of-Thought-for-LLM-Fine-Tuning
PyReason-7k: Advanced Python Chain-of-Thought Dataset
Dataset Description
This dataset contains 7,000+ high-quality Python programming examples designed for LLM fine-tuning.
Each entry includes a detailed thought_process (Chain-of-Thought) to teach models logical reasoning before coding.
Key Features:
Chain-of-Thought: Step-by-step reasoning traces.
Error Handling: Solutions include try-except blocks and logging.
Diverse Tasks: Algorithms, API handling, Data Structures.… See the full description on the dataset page: https://huggingface.co/datasets/xTayyub/High-Quality-Synthetic-Python-Dataset-with-Reasoning-Traces-Chain-of-Thought-for-LLM-Fine-Tuning.team-truthowl-mixed-reasoning-dataset
Team P11 Mixed Reasoning Dataset
📊 Dataset description
HLE（Humanity's Last Exam）向けに作成した、数学中心＋科学MCの混合推論データセットです。
推論過程（Chain-of-Thought）を保持し、最終解答の正規化を行っています。
対象モデルは DeepSeek-R1-Distill-Qwen-32B、学習はQLoRAを想定しています。
🎯 Purpose
Competition: 松尾研LLMコンペ 2025
Target Model: DeepSeek-R1-Distill-Qwen-32B
Training Method: QLoRA Fine-tuning（4bit NF4, double quant）
📦 Composition
Math Hard（MATH Level≥3, HARDMath）
Math Mid（GSM8K, MetaMathQA）
Science（GPQA… See the full description on the dataset page: https://huggingface.co/datasets/weblab-llm-competition-2025-bridge/team-truthowl-mixed-reasoning-dataset.