datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gspc-oss
GSPC — openness bank (OSSBench)
Council of AI measurement bank. Measurement, not certification.
Bank. Frozen split. Live n is the matching axis on GET https://councilof.ai/api/gspc, not a Hub score. Not a certificate. Art 50 (EUR-Lex): 2 August 2026 live; marking grace 2 December 2026.
Live measurement. This bank stands behind the openness row of the live GSPC board: GET https://councilof.ai/api/gspc?axis=openness (family, kind, status and n are on that row, never typed here;… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-oss.gpt-oss-120b-reasoning-STEM-5K
GPT-OSS-120B-Distilled-Reasoning-STEM Dataset
1) Dataset Overview
Data Source Model: gpt-oss-120b-high
Task Type: STEM Reasoning and Problem Solving (Science, Technology, Engineering & Mathematics)
Data Format: `JSON Lines
Fields: generator, category, input, CoT_Native——reasoning, answer
(Consistent with the math dataset, splitting the original 'output' into 'reasoning' and 'answer' for COT/SFT scenarios.)
2) Design Goals (Motivation)
This dataset targets… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/gpt-oss-120b-reasoning-STEM-5K.Health-Bench-Eval-OSS-2025-07
Dataset Card for HealthBench
Dataset Summary
HealthBench is a benchmark dataset developed by OpenAI in collaboration with 262 physicians from 60 countries to evaluate AI systems in health-related conversational scenarios. It contains 5,000 multi-turn health conversations in a JSONL file (2025-05-07-06-14-12_oss_eval.jsonl), simulating interactions between AI models and users (laypersons or clinicians). Each conversation includes a user prompt, a candidate model response… See the full description on the dataset page: https://huggingface.co/datasets/Tonic/Health-Bench-Eval-OSS-2025-07.GPT-OSS-120B-Distilled-Reasoning-math
GPT-oss-120B-Distilled-Reasoning-math Dataset
Data Source Model: gpt-oss-120bTask Type: Mathematical Problem SolvingData Format: JSON Lines
Fields: Generator, Category, Input, CoT_Native_Reasoning, Reasoning, Answer
Core Statistics
Generated complete reasoning processes and answers using gpt-oss-120b (MXFP4).The text length of the dataset reflects the depth and complexity of its content. I have statistically analyzed the lengths of the input (question), Reasoning, and… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GPT-OSS-120B-Distilled-Reasoning-math.gpt-oss-120B-distilled-reasoning
GPT-oss-120B-Distilled-Reasoning-math Dataset
Data Source Model: gpt-oss-120bTask Type: Mathematical Problem SolvingData Format: JSON Lines
Fields: Generator, Category, Input, Output
Core Statistics
Generated complete reasoning processes and answers using gpt-oss-120b (MXFP4).The text length of the dataset reflects the depth and complexity of its content. I have statistically analyzed the lengths of the input (question), Reasoning, and Answer.To understand the data… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/gpt-oss-120B-distilled-reasoning.MuSeR_GPT_OSS_120B_DistillationThis dataset contains ~100k synthetic medical queries and corresponding responses distilled from GPT-OSS-120B.
The generation of synthetic medical queries follows an attribute-conditioned generation method proposed in paper Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning.
We found that supervised fine-tuning on this dataset can substantially improve LLMs' medical conversational capabilities. See our paper and project page for more details.
If… See the full description on the dataset page: https://huggingface.co/datasets/zyx1234/MuSeR_GPT_OSS_120B_Distillation.GPT-OSS-20B-Distilled-Reasoning-Mini
Dataset Card for Dataset Name
GPT-OSS-20B Distilled Reasoning Dataset Mini
(Multi-stage Evaluative Refinement Method for Reasoning Generation)
Dataset Details and Description
This is a high-quality instruction fine-tuning dataset constructed through knowledge distillation, featuring detailed Chain-of-Thought (CoT) reasoning processes. The dataset is designed to enhance the capabilities of smaller language models in complex reasoning, logical analysis, and instruction… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GPT-OSS-20B-Distilled-Reasoning-Mini.gpt-oss-120B-distilled-math-OpenAI-Harmony
📚 Dataset Overview
Data Source Model: gpt-oss-120bTask Type: Mathematical Problem SolvingData Format: JSON Lines (.jsonl)Fields: Generator, Category, Input, Output
Note: If you are using this template for training, please make sure the format is correct before starting.Since this template is still under continuous improvement and learning, it may not be fully complete yet. I appreciate your understanding.
📈 Core Statistics
Generated complete reasoning processes… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/gpt-oss-120B-distilled-math-OpenAI-Harmony.MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA
Dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA is an English open-source software issue question-answering and retrieval benchmark. Each example asks a question grounded in one GitHub issue and requires evidence from a related issue. The data contains explicit cross-issue references and a three-document silver evidence path.
Dataset configurations
Configuration
Splits
Rows… See the full description on the dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA.buddha_oss_dataset
장아함경 Buddha QA Dataset (Complete) / Agama Sutra Buddha QA Dataset
한국어 설명 | English Description
한국어
🙏 개요
이 데이터셋은 장아함경(長阿含經) 제1-3권 전체를 기반으로 생성된 한국어 불교 질문-답변 데이터셋입니다. GPT-4.1-2025-04-14 모델과 고급 추론 시스템(fill_thinking.py)을 사용하여 생성되었으며, 현대인이 이해하기 쉽도록 해석된 붓다의 가르침을 포함합니다.
📊 데이터셋 통계
총 QA 쌍: 335개
훈련 데이터: 268개 (80%)
검증 데이터: 67개 (20%)
출처: 장아함경 제1-3권 (총 70개 경전)
생성 모델: GPT-4.1-2025-04-14
추론 시스템: fill_thinking.py 기반
🔧 데이터 형식
각 레코드는 다음과 같은 3-message… See the full description on the dataset page: https://huggingface.co/datasets/LeBrony/buddha_oss_dataset.filtered-awesome-chatgpt-propmts-oss-120b
Filtered Awesome ChatGPT Prompts – Model Outputs Dataset
Overview
This dataset contains model-generated responses to prompts from the fka/awesome-chatgpt-prompts Hugging Face dataset.
Each prompt was sent to the openai/gpt-oss-120b model via the OpenRouter API.
The resulting dataset was then filtered to remove:
Non English outputs with high language-detection confidence (fastText score < 0.7)
Very short outputs (≤ 10 words)
The goal of this dataset is to provide a… See the full description on the dataset page: https://huggingface.co/datasets/Hugodonotexit/filtered-awesome-chatgpt-propmts-oss-120b.