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-Qwen-32B-LeaPPaper: Learning from Peers in Reasoning Models
Project Page: https://learning-from-peers.github.io/
Code: https://github.com/tongxuluo/LeaP
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.deepseek-r1-reasoning-promptsI created a reasoning prompt dataset from deepseek-r1 model with the purpose of fine-tuning small language models to use them to generate better
reasoning prompt to use with bigger llm models.
Metadata
The metadata is made available through a series of parquet files with the following schema:
id: A unique identifier for the qa.
question:
answer: Answer from deepseek-r1 think model.
reasoning: Reasoning from deepseek-r1 model.
DeepSeek-R1-Distill-Data-5k