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-7B_eval_d81a
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_d81a
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
MMLUPro
HMMT
HLE
AIME25
LiveCodeBenchv5
Accuracy
43.4
25.0
12.4
36.0
34.5
MMLUPro
Accuracy: 43.38%
Accuracy
Questions Solved
Total Questions
43.38%
N/A
N/A
HMMT
Average Accuracy: 25.00% ± 1.72%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_d81a.DeepSeek-R1-Distill-Qwen-7B_eval_118b
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_118b
Precomputed model outputs for evaluation.
Evaluation Results
LiveCodeBenchv5_official
Average Accuracy: 31.18% ± nan%
Number of Runs: 1
Run
Accuracy
Questions Solved
Total Questions
1
31.18%
87
279
DeepSeek-R1-Distill-Qwen-7B_eval_03-07-25_17-55_0981
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_03-07-25_17-55_0981
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AIME25
AMC23
GPQADiamond
MATH500
Accuracy
42.7
22.7
67.0
33.3
79.6
AIME24
Average Accuracy: 42.67% ± 4.75%
Number of Runs: 5
Run
Accuracy
Questions Solved
Total Questions
1
50.00%
15
30
2
26.67%
8
30
3
53.33%
16
30
4
50.00%
15
30
5
33.33%
10
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_03-07-25_17-55_0981.DeepSeek-R1-Distill-Qwen-1.5B_eval_5554
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-1.5B_eval_5554
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
HLE
HMMT
AIME25
LiveCodeBenchv5
Accuracy
32.7
71.8
80.8
31.1
32.5
31.1
27.2
8.8
8.5
15.0
15.3
23.7
15.4
AIME24
Average Accuracy: 32.67% ± 2.39%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/DeepSeek-R1-Distill-Qwen-1.5B_eval_5554.DeepSeek-R1-only-CoTdeepseek-r1-distill-cotCreated by several different models:
DeepSeek R1
DeepSeek R1 Distill Qwen 14B
Qwen3.8 27B
format:
{"quest": ..., "aswer": "..."}
ty
DeepSeek-R1-Distill-Qwen-7B_eval_c64a
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_c64a
Precomputed model outputs for evaluation.
Evaluation Results
LiveCodeBenchv5_v3
Average Accuracy: 30.47% ± 0.69%
Number of Runs: 3
Run
Accuracy
Questions Solved
Total Questions
1
31.34%
84
268
2
30.97%
83
268
3
29.10%
78
268
DeepSeek-R1-Distilled-Translate-en-zh_CN-39k-Alpaca-GPT4
DeepSeek R1 满血蒸馏英中翻译数据集 Alpaca GPT-4（带 CoT 版本）
本数据集是 @FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k 的 Alpaca GPT-4 版本，专门用于微调语言模型的英中翻译任务。采用标准的指令微调格式，更适合直接用于 SFT（Supervised Fine-tuning）训练。
本项目主要基于以下工具完成数据处理和生成：
llm-tools: 用于大语言模型数据处理的工具集合
数据集概览
关键统计
总样本数：38,981
数据集结构
字段说明
features:
- name: instruction # 待翻译的英文文本
dtype: string
- name: input # 空字符串，保持与标准指令格式一致
dtype: string
- name: output #… See the full description on the dataset page: https://huggingface.co/datasets/FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k-Alpaca-GPT4.DeepSeek-R1-Distilled-Translate-en-zh_CN-39k
DeepSeek R1 满血蒸馏英中翻译数据集
本数据集是一个专门用于微调语言模型的英中翻译数据集，主要通过DeepSeek R1满血版蒸馏完成。
SFT训练版本
为了方便直接进行监督微调（Supervised Fine-tuning，SFT）训练，我们提供了两个使用标准 instruction-input-output 格式预的处理版本：
带 CoT 版本
保留了翻译过程中的思维链（Chain of Thought）
适合训练具有推理能力的翻译模型
无 CoT 版本
移除了思维链部分，只保留最终翻译结果
更适合训练直接输出翻译结果的模型
数据更简洁，训练更高效
项目依赖
本项目主要基于以下工具完成数据处理和生成：
llm-tools: 用于大语言模型数据处理的工具集合
qa-generator: 基于大语言模型的问答数据生成工具
数据集概览
关键统计
总样本数：38,981
数据集结构
字段说明… See the full description on the dataset page: https://huggingface.co/datasets/FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k.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-Distill-Qwen-32B_NUMINA_train_amc_aime-llama3.1deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel
Dataset Card for deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel/raw/main/pipeline.yaml"… See the full description on the dataset page: https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-6-blocks-self-probing-state-distilabel.DeepSeek-R1-Distill-Qwen-7B_eval_d54a
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_eval_d54a
Precomputed model outputs for evaluation.
Evaluation Results
LiveCodeBenchv5total
Average Accuracy: 43.33% ± 0.20%
Number of Runs: 3
Run
Accuracy
Questions Solved
Total Questions
1
43.64%
384
880
2
43.41%
382
880
3
42.95%
378
880
DeepSeek-R1-Distill-Qwen-7B_OpenThoughts3_eval_8179
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_OpenThoughts3_eval_8179
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
HMMT
Accuracy
64.0
91.2
89.0
65.4
47.3
61.8
23.3
24.2
51.3
10.9
45.5
35.3
AIME24
Average Accuracy: 64.00% ± 1.23%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/DeepSeek-R1-Distill-Qwen-7B_OpenThoughts3_eval_8179.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_code_1kDeepSeek-R1-7B-MathPhysics-V6-evaldeepseek_R1_0528_mathThe dataset is sourced from https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-0528-Distilled, with math-related samples filtered out based on the model's confidence (lowest ppl).
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-Qwen-1.5B-pts-thought-anchors
PTS Thought Anchors Dataset
A dataset of thought anchors - critical reasoning steps - identified using the Thought Anchors technique from the PTS tool.
Details
Source: Generated using the PTS tool
Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Tags: pts, thought-anchors, reasoning, llm-analysis
Dataset Structure
This dataset contains thought anchors identified from reasoning traces. Each anchor represents a sentence that significantly impacts the success… See the full description on the dataset page: https://huggingface.co/datasets/codelion/DeepSeek-R1-Distill-Qwen-1.5B-pts-thought-anchors.DeepSeek-R1-20k
Rethinking Generalization in Reasoning SFT
This repository contains datasets associated with the paper "Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability".
The research investigates the factors influencing cross-domain generalization in Large Language Models (LLMs) during reasoning-focused supervised fine-tuning (SFT) with long chain-of-thought (CoT) data.
Key Findings
Optimization Dynamics: Cross-domain… See the full description on the dataset page: https://huggingface.co/datasets/jasonrqh/DeepSeek-R1-20k.deepseek-r1-qwen-32b-planning-4-blocks-self-probing-state-distilabel
Dataset Card for deepseek-r1-qwen-32b-planning-4-blocks-self-probing-state-distilabel
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-4-blocks-self-probing-state-distilabel/raw/main/pipeline.yaml"… See the full description on the dataset page: https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-4-blocks-self-probing-state-distilabel.deepseek-r1-systems-kernel-reasoning
🧠 DeepSeek-R1 Low-Level Systems & Kernel Reasoning Suite (2026)
🛒 Commercial Full Suite Available:
The full production suite with 10,000 SFT Hardware Reasoning Traces + 2,500 High-Contrast DPO Alignment Pairs across all 20 domains is available on Gumroad:
👉 Download Full Commercial Dataset on Gumroad (Starter \ / Pro \ / Enterprise )
A Tier-1 Commercial Dataset Suite engineered specifically for fine-tuning DeepSeek-R1, DeepSeek-R1-Distill-Qwen-14B/32B, and frontier… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/deepseek-r1-systems-kernel-reasoning.Deepseek-R1-ERP-DatasetDataset ERP dataset generated from deepseek R1. This was a bit intensive. I write a python scrip that would generate a random plot, the ERP theme of which was chosing at random from about 20 different types of themes (One of the more tame ones, for example, "Romance Erotica"). I would then guide the inference by using the "user" role to switch perspectives between the two characters, and continue the story.
I then wrote another script that used deepseek-cat to fix the reasoning portions.… See the full description on the dataset page: https://huggingface.co/datasets/SuperbEmphasis/Deepseek-R1-ERP-Dataset.DeepSeek-R1-Distill-Qwen-1.5B_OpenThoughts3_eval_5554
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-1.5B_OpenThoughts3_eval_5554
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
HMMT
Accuracy
0.0
2.2
1.6
7.0
16.2
10.3
10.0
0.9
1.3
0.7
5.6
12.8
0.0
AIME24
Average Accuracy: 0.00% ± 0.00%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/DeepSeek-R1-Distill-Qwen-1.5B_OpenThoughts3_eval_5554.Deepseek-R1-Reasoning-ERP-Limiter-TestThis is an experiment. For RP, sometimes the thinking that Deepseek and others do are a bit much. Since I Now have a rather large (ish) deepseek R1 dataset. I made a python script to count the number of words from the reasoning, round up to the nearest 50 (So if the number was 416, this would be set to 450), and then add some text in the system prompt and the reasoning.
My goal is to easily, via a system prompt, be able to control the size/amount of tokens from thinking.
deepseek-r1-qwen-32b-planning-mystery-16k
Dataset Card for deepseek-r1-qwen-32b-planning-mystery-16k
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-mystery-16k/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info… See the full description on the dataset page: https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-32b-planning-mystery-16k.deepseek_r1_zh 完全从dolphin-r1正则化清洗出的r1中文对话数据集，完全由r1-671b模型生成的高质量数据集，可以用于中文模型微调蒸馏。
DeepSeek-R1-Distill-Qwen-1.5B_eval_2e29
mlfoundations-dev/DeepSeek-R1-Distill-Qwen-1.5B_eval_2e29
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
Accuracy
33.3
72.5
81.8
20.6
32.7
25.8
27.3
7.2
8.0
21.7
8.6
17.5
AIME24
Average Accuracy: 33.33% ± 2.75%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/DeepSeek-R1-Distill-Qwen-1.5B_eval_2e29.DeepSeek-R1-Distill-Qwen-1.5-difficulty