datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
olm-CC-MAIN-2022-49-sampling-ratio-olm-0.15114822547
Dataset Card for OLM November/December 2022 Common Crawl
Cleaned and deduplicated pretraining dataset, created with the OLM repo here from 15% of the November/December 2022 Common Crawl snapshot.
Note: last_modified_timestamp was parsed from whatever a website returned in it's Last-Modified header; there are likely a small number of outliers that are incorrect, so we recommend removing the outliers before doing statistics with last_modified_timestamp.
mc4-samplingA sampling-enabled version of mC4, the colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is a version of the processed version of Google's mC4 dataset by AllenAI, in which sampling methods are implemented to perform on the fly.dsp-fft-sampling-aliasing
Synthetic DSP Dataset: FFT + Sampling / Aliasing
This repository contains synthetic instruction-style DSP samples
designed for numerical reasoning and conceptual understanding of
Digital Signal Processing (DSP) fundamentals.
The dataset focuses on:
FFT bin reasoning and frequency-domain interpretation
Sampling theory
Aliasing effects
Dataset Origin & Verification
This dataset was generated as part of the project:
Fine-Tuning Lightweight Large Language Models for a… See the full description on the dataset page: https://huggingface.co/datasets/Irfanuruchi/dsp-fft-sampling-aliasing.scas_verified_teacher_pool
SCAS Verified Teacher Answer Pool
This dataset provides an aligned, correctness-verified pool of
teacher-generated mathematical reasoning solutions for studying
student-centric data selection in distillation.
The release covers two source corpora, Hendrycks MATH and DeepScaleR. For each
corpus, we retain the subset of questions on which all nine selected teacher
models produce verified correct answers. Each retained question is paired with
nine alternative teacher solutions, one… See the full description on the dataset page: https://huggingface.co/datasets/Student-Centric-Answer-Sampling/scas_verified_teacher_pool.rejection-sampling-QA
Rejecction Sampling Q&A
This dataset is a very small curated question-answer pairs.
The questions were hand-crafted to test the model's capabilities to follow instruction across various domains.
The answers were generated using Microsoft's Phi-2 and curated using OpenAssistant's Large DeBERTa v3 Reward Model v2.
Dataset Details
The answers of this dataset were generated by prompting Microsoft's Phi-2 using a prompt format inspired by Stanford's Alpaca to help the LLM… See the full description on the dataset page: https://huggingface.co/datasets/alehc/rejection-sampling-QA.clinical-quad-pk-sampling-window-deviation-bioanalytical-variance-dose-adjustment-interim-v0.1Clarus Clinical Quad Coupling PK Integrity v0.1
PurposeDetect PK integrity distortion driven by four interacting nodes.
Quad nodes
Sampling window deviation
Bioanalytical or stability variance
Dose adjustment decisions
Governance interim or submission timing
InputOne vignette.
OutputStrict JSON only.
Required keys
pk_integrity_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale
confidence
Filesdata/train.csvdata/test.csvscorer.py
Run scoringCreate… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-pk-sampling-window-deviation-bioanalytical-variance-dose-adjustment-interim-v0.1.R1-Distill-Qwen-14B-MATH_training500-sampling64
Model : deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Original Dataset : MATH - first 500 queries in training split
Prompt:
{"role": "user", "content": "Please reason step by step, and put your final answer within \boxed{}." + '\n\n' + problem + '\n<think>\n'}
Sampling Parameters :
num_sampling=64
max_tokens=32768
temperature=0.6
top_p=0.95
‘correct’ : computed by the code in the link… See the full description on the dataset page: https://huggingface.co/datasets/HaeChan0305/R1-Distill-Qwen-14B-MATH_training500-sampling64.demo_rejection_sampling_QA_phi-2_deberta-v3-large-v2_temp0.2This is a demo constructed dataset for alignment/preference learning.
With paritially handcrafted questions (prompts), the answers are genreated by the phi-2 model with temperature 0.2 and the answers are scores select by the deberta-large-v2.
The dataset containing questions and the selected answers from highest to lowest, decoding with rejection sampling K=8.
Example loading:
import datasets
ds = datasets.load_dataset('yizhilll/demo_rejection_sampling_QA_phi-2_deberta-v3-large-v2_temp0.2')… See the full description on the dataset page: https://huggingface.co/datasets/yizhilll/demo_rejection_sampling_QA_phi-2_deberta-v3-large-v2_temp0.2.Qwen3-0.6B-AIME-2023-2024-2025-sampling64
Model : Qwen3-0.6B
Original Dataset : AIME2023, AIME2024, AIME2025
Prompt:
{"role": "user", "content": "Please reason step by step, and put your final answer within \boxed{}." + '\n\n' + problem}
Sampling Parameters :
num_sampling=64
max_tokens=38912
temperature=0.6
top_p=0.95
top_k=20
min_p=0
‘correct’ : computed by the code in the link (https://github.com/LeapLabTHU/Absolute-Zero-Reasoner/blob/master/absolute_zero_reasoner/rewards/math_utils.py)
R1-Distill-Qwen-1.5B-MATH_training500-sampling64
Model : deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Original Dataset : MATH - first 500 queries in training split
Prompt:
{"role": "user", "content": "Please reason step by step, and put your final answer within \boxed{}." + '\n\n' + problem + '\n<think>\n'}
Sampling Parameters :
num_sampling=64
max_tokens=32768
temperature=0.6
top_p=0.95
‘correct’ : computed by the code in the link… See the full description on the dataset page: https://huggingface.co/datasets/HaeChan0305/R1-Distill-Qwen-1.5B-MATH_training500-sampling64.rejection_sampling_phi_2_OA_rm
Dataset Card for Rejection Sampling Phi-2 with OpenAssistant RM
Dataset Summary
The "Rejection Sampling Phi-2 with OpenAssistant RM" dataset consists of 10 pairs of prompts and responses, which were generated using rejection sampling over 10 Phi-2 generation using the OpenAssistant Reward Model.
Supported Tasks and Leaderboards
The dataset and its creation rationale could be used to support models for question-answering, text-generation, or conversational… See the full description on the dataset page: https://huggingface.co/datasets/alizeepace/rejection_sampling_phi_2_OA_rm.Qwen3-32B-AIME-2023-2024-2025-sampling64
Model : Qwen3-32B
Original Dataset : first 24 queries in AIME2023 (시간 없어서 뒤에꺼 못함.)
Prompt:
{"role": "user", "content": "Please reason step by step, and put your final answer within \boxed{}." + '\n\n' + problem}
Sampling Parameters :
num_sampling=64
max_tokens=38912
temperature=0.6
top_p=0.95
top_k=20
min_p=0
‘correct’ : computed by the code in the link (https://github.com/LeapLabTHU/Absolute-Zero-Reasoner/blob/master/absolute_zero_reasoner/rewards/math_utils.py)
Qwen3-32B-MATH_training500-sampling64
Model : Qwen3-32B
Original Dataset : MATH - first 500 queries in training split
Prompt:
{"role": "user", "content": "Please reason step by step, and put your final answer within \boxed{}." + '\n\n' + problem}
Sampling Parameters :
num_sampling=64
max_tokens=38912
temperature=0.6
top_p=0.95
top_k=20
min_p=0
‘correct’ : computed by the code in the link (https://github.com/LeapLabTHU/Absolute-Zero-Reasoner/blob/master/absolute_zero_reasoner/rewards/math_utils.py)
Qwen3-0.6B-MATH-sampling64
Model : Qwen3-0.6B
Original Dataset : MATH
train : first 500 queries in training split
test : MATH500
Prompt:
{"role": "user", "content": "Please reason step by step, and put your final answer within \boxed{}." + '\n\n' + problem}
Sampling Parameters :
num_sampling=64
max_tokens=38912
temperature=0.6
top_p=0.95
top_k=20
min_p=0
‘correct’ : computed by the code in the link (https://github.com/LeapLabTHU/Absolute-Zero-Reasoner/blob/master/absolute_zero_reasoner/rewards/math_utils.py)
Qwen2.5-MATH-1.5B-MATH-sampling8
Model : Qwen2.5-MATH-1.5B
Original Dataset : MATH
train : 12K
test : 500
Prompt:
[
{"role": "system", "content": "Please reason step by step, and put your final answer within \boxed{}."},
{"role": "user", "content": problem + "\n\nLet's think step by step and output the final answer within \boxed{}."}
]
Sampling Parameters :
num_sampling=8
max_tokens=4048
temperature=0.7
top_p=0.8
‘correct’ : computed by the code in the link… See the full description on the dataset page: https://huggingface.co/datasets/HaeChan0305/Qwen2.5-MATH-1.5B-MATH-sampling8.