datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DeepSeek-R1-Distill-Qwen-1.5B-Self-CalibrationThis dataset contains data for the paper Efficient Test-Time Scaling via Self-Calibration.
We propose an efficient test-time scaling method by using model confidence for dynamically sampling adjustment, since confidence can be seen as an intrinsic measure that directly reflects model uncertainty on different tasks. For example, we can incorporate the model’s confidence into self-consistency by assigning each sampled response $y_i$ a confidence score $c_i$. Instead of treating all responses… See the full description on the dataset page: https://huggingface.co/datasets/HINT-lab/DeepSeek-R1-Distill-Qwen-1.5B-Self-Calibration.deepseek-r1-autonomous-math-logic-cot-2026
📐 Enterprise DeepSeek-R1 Autonomous Mathematical & Logic CoT SFT/DPO Dataset (2026)
High-precision multi-turn instruction tuning and preference optimization dataset with step-by-step hypothesis exploration, error discovery, and dynamic backtracking Chain-of-Thought (<thought>) reasoning trees for fine-tuning LLMs (DeepSeek-R1-Distill-Qwen, Qwen-2.5-Math, Llama-3.3, Mistral) into World-Class Olympiad Mathematicians and Formal Verification Agents.
📊 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/deepseek-r1-autonomous-math-logic-cot-2026.DeepSeek-r1-Distill-Bangla-MMLU-Reasoning-DataDeepSeek R1 Bangla MMLU Distil Dataset
Original Dataset: hishab/bangla-mmlu
Train Samples: 17,796
Test Samples: 2,576
Total API Cost: 7K BDT
Contributors:
Myself
Numaer
How the Dataset was created
Step 1 - Base Dataset
I've used bangla-mmlu dataset released by hisab. Kudos to them for creating and open sourcing the dataset. Without their dataset this synthetic reasoning dataset won't exist in the first place.
Step 2 - Select Subset
Since I'm… See the full description on the dataset page: https://huggingface.co/datasets/KillerShoaib/DeepSeek-r1-Distill-Bangla-MMLU-Reasoning-Data.Chinese-DeepSeek-R1-Distill-data-110k-opencc
中文基於滿血DeepSeek-R1蒸餾數據集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
本資料集由 Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT 經過 opencc 轉換而成，再次感謝原作者。
注意：該版本為，可以直接SFT使用的版本，將原始數據中的思考和答案整合成output字段，大部分SFT代碼框架均可直接直接加載訓練。
本數據集為中文開源蒸餾滿血R1的數據集，數據集中不僅包含math數據，還包括大量的通用類型數據，總數量為110K。
為什麽開源這個數據？
R1的效果十分強大，並且基於R1蒸餾數據SFT的小模型也展現出了強大的效果，但檢索發現，大部分開源的R1蒸餾數據集均為英文數據集。 同時，R1的報告中展示，蒸餾模型中同時也使用了部分通用場景數據集。
為了幫助大家更好地覆現R1蒸餾模型的效果，特此開源中文數據集。… See the full description on the dataset page: https://huggingface.co/datasets/benchang1110/Chinese-DeepSeek-R1-Distill-data-110k-opencc.math500-deepseek-r1-distill-qwen-1.5b
Dataset Card for "math500-deepseek-r1-distill-qwen-1.5b"
Dataset Summary
This dataset is a distilled version of the MATH500 dataset, augmented with reasoning-based responses generated by the deepseek-r1-distill-qwen-1.5b language model. The dataset is designed to evaluate and improve the mathematical reasoning capabilities of LLMs through step-by-step solutions and final answers.
Each example consists of:
The original problem statement from MATH500
The reference solution… See the full description on the dataset page: https://huggingface.co/datasets/jsm0424/math500-deepseek-r1-distill-qwen-1.5b.math500-deepseek-r1-distill-qwen-14b
Dataset Card for "math500-deepseek-r1-distill-qwen-14b"
Dataset Summary
This dataset is a distilled version of the MATH500 dataset, augmented with reasoning-based responses generated by the deepseek-r1-distill-qwen-14b language model. The dataset is designed to evaluate and improve the mathematical reasoning capabilities of LLMs through step-by-step solutions and final answers.
Each example consists of:
The original problem statement from MATH500
The reference solution… See the full description on the dataset page: https://huggingface.co/datasets/jsm0424/math500-deepseek-r1-distill-qwen-14b.AM-DeepSeek-R1-Distilled-1.4M_preprocess
AM-DeepSeek-R1-Distilled-1.4M_preprocess
Preprocessed from [a-m-team/AM-DeepSeek-R1-Distilled-1.4M] (configs: am_0.5M, am_0.9M).
Two columns:
question
answer: reasoning enclosed in <think>...</think> and final in #### answer.
Format:
<think> ...reasoning... </think>
#### answer ...final...