datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DeepSeek-v4-Pro-AgentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
DeepSeek v4 Pro Agent Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by deepseek/deepseek-v4-pro.
JSONL files: 4006
Training-ready tools
A complete configured tools schema snapshot is embedded in the collapsed section at the bottom of… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/DeepSeek-v4-Pro-Agent.AM-DeepSeek-Distilled-40MFor more open-source datasets, models, and methodologies, please visit our GitHub repository and paper: DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training.
Due to certain constraints, we are only able to open-source a subset of the complete dataset.
Model Training Performance based on our complete dataset
On AIME 2024, our 72B model achieved a score of 79.2 using only supervised fine-tuning (SFT). The 32B model reached 75.8 and… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-Distilled-40M.DeepSeek-v4-Pro-AgentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
DeepSeek v4 Pro Agent Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by deepseek/deepseek-v4-pro.
JSONL files: 4006
Training-ready tools
A complete configured tools schema snapshot is embedded in the collapsed section at the bottom of… See the full description on the dataset page: https://huggingface.co/datasets/ronaldcmz/DeepSeek-v4-Pro-Agent.DeepSeek-V4-Pro-Distilled-200K
DeepSeek‑V4‑Pro‑Distilled‑200K
High-quality Math & STEM reasoning distilled from DeepSeek‑V4‑Pro in Max mode
Reasoning traces · Proofs · Verification · Mathematics · Physics · Chemistry · Biology
Overview
DeepSeek‑V4‑Pro‑Distilled‑200K is a supervised fine-tuning collection of long-form mathematical and scientific reasoning. Its responses were generated with DeepSeek‑V4‑Pro in Max inference mode, then normalized into a compact conversational… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Pro-Distilled-200K.Chinese-DeepSeek-R1-Distill-data-110k
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。
该中文数据集中的数据分布如下：… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k.DeepSeek-V4-Distill-8000x
🐳 DeepSeek-V4-Distill-8100x
Dataset Summary
DeepSeek-V4-Distill-8100x is a supervised fine-tuning dataset for reasoning-oriented distillation. The question prompts come from Jackrong/GLM-5.1-Reasoning-1M-Cleaned, and the answers were generated by the teacher model DeepSeek-V4-Flash.
After the cleaning process, the released train split contains 7,716 high-quality JSONL examples.
[!NOTE]
The answer pool was cleaned to remove real-time questions… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Distill-8000x.DeepSeek-v4-Pro-AgentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
DeepSeek v4 Pro Agent Traces
This directory contains raw agent trace files generated by teich.
All assistant responses were generated by deepseek/deepseek-v4-pro.
JSONL files: 4006
Training-ready tools
A complete configured tools schema snapshot is embedded in the collapsed section at the bottom of… See the full description on the dataset page: https://huggingface.co/datasets/hardcoremoore/DeepSeek-v4-Pro-Agent.Chinese-DeepSeek-R1-Distill-data-110k-SFT
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：该版本为，可以直接SFT使用的版本，将原始数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT.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-V4-Flash-0731-Teacher-Distillation-40513x
DeepSeek V4 Flash 0731 Teacher Distillation — 40,513 Retained Rows
Teacher-distillation corpus generated with
deepseek-ai/DeepSeek-V4-Flash-0731.
The original manifest contained 45,000 unique seeds.
Following generation, QC, retry-based repair, quarantine auditing,
and recovery adjudication, 40,513 rows were retained.
Composition
Bucket
Rows
Coding
5,601
Agentic
9,982
Cyber blue
13,000
Controlled cyber red
6,999
Tool use
4,931
Total
40,513… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x.deepseek-v4-pro-max-distill-1k
Overeview
This dataset contains reasoning traces and final answers generated by DeepSeek-V4-Pro
(reasoning_effort=max, thinking.enabled=true) using prompts sampled from
Jackrong/GLM-5.1-Reasoning-1M-Cleaned.
Goal: just check quality
Update: The dataset have fully 1000 samples in 04/27/2026 cost only ~$5.46
Planning: try out another distill style such as roleplay
Why DeepSeek-V4-Pro instead of OpenAI / Anthropic?
For distillation, the teacher must expose… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/deepseek-v4-pro-max-distill-1k.AM-DeepSeek-R1-Filtered-Math-Code
AM DeepSeek R1 Filtered Math and Code
This repository publishes reproducible training subsets derived from
a-m-team/AM-DeepSeek-R1-Distilled-1.4M
at pinned revision 53531c06634904118a2dcd83961918c4d69d1cdf.
The source dataset and this derived release use CC BY-NC 4.0. Commercial use is
not permitted by that license. Preserve attribution and review the upstream
dataset card before use.
Contents
Config / split
Records
Bytes
SHA-256
math / train
111,657
2… See the full description on the dataset page: https://huggingface.co/datasets/chhao/AM-DeepSeek-R1-Filtered-Math-Code.swebench-verified-deepseek-v4-flash-failure-analysis
SWE-bench Verified runs & failure analysis — DeepSeek-V4-flash (local) × mini-swe-agent
Per-instance analysis of SWE-bench Verified runs of a locally-served DeepSeek-V4-flash model
driven by mini-swe-agent, graded with the official
SWE-bench harness. Each instance carries the full agent trajectory, a readable transcript, the
submitted patch, the harness test output, deterministic metrics, and a hand-verified qualitative
root-cause diagnosis.
Current numbers (resolve rates… See the full description on the dataset page: https://huggingface.co/datasets/daaain/swebench-verified-deepseek-v4-flash-failure-analysis.Math-Chinese-DeepSeek-R1-10K
中文 DeepSeek-R1-Distil 数学指令微调数据集
💻 Github Repo
基本信息
数据集大小 10K，独立生成指令与回复，并非其他社区数据集的子集。所有数据经过校验，答案正确性可以得到保证。
数据集的组成如下：
问题类型
数据条数
定积分计算
2626
多项式化简
1621
因式分解
2557
多项式展开
2095
多项式方程
1101
总数
10000
数据格式
每条数据的格式如下：
{
"id": <<12位nanoid>>,
"prompt": <<提示词>>,
"reasoning": <<模型思考过程>>,
"response": <<模型最终回复>>
}
DeepSeek-V4-Pro-Reasoning-8000x
DeepSeek-V4-Pro-Reasoning-8000x
This dataset contains 8,014 synthetic reasoning examples generated with DeepSeek V4 Pro through the DeepSeek API.
The release is branded as 8000x for readability, while the exact row count is 8,014.
This dataset is designed for supervised fine-tuning, reasoning distillation, and experimentation with long-form visible reasoning traces.
Dataset Summary
Release label: 8000x
Actual rows: 8,014
Teacher model: DeepSeek-V4-Pro… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/DeepSeek-V4-Pro-Reasoning-8000x.MathCOT-oss-vs-DeepSeek
Learning to Reason: Training LLMs with GPT-OSS or DeepSeek R1 Reasoning Traces
This dataset is the one used from the paper, available here 📄
This dataset consists of 242k math questions, with the verified generated answer (with reasoning) by both DeepSeek-R1-0528 and gpt-oss-120b.
The original prompts and the DeepSeek-R1-0528 traces were taken from NVIDIA's Nemotron-Post-Training-Dataset-v1.
Citation
If you found this dataset useful, please cite the paper below:… See the full description on the dataset page: https://huggingface.co/datasets/dicta-il/MathCOT-oss-vs-DeepSeek.deepseek-v4-pro-math-cot-1k
DeepSeek V4 Pro Math CoT 1K
A small, high-signal supervised-fine-tuning (SFT) dataset of math reasoning traces. Problems were sampled from a Nemotron math problem set (originally sourced from StackExchange-Math and AoPS), answered by DeepSeek V4 Pro with thinking enabled at high reasoning effort, then independently reviewed by DeepSeek V4 Flash for correctness against the expected answer. Pathological reasoning traces (looping, run-away length, excessive Wait-style backtracking)… See the full description on the dataset page: https://huggingface.co/datasets/blythet/deepseek-v4-pro-math-cot-1k.s1K-1.1-deepseek-cot
s1K-1.1 (DeepSeek-R1 traces) — format cho SegmentSelectiveSFT
Chuyen doi tu simplescaling/s1K-1.1
bang prepare_s1k.py (default flags) trong repo SegmentSelectiveSFT.
Moi dong jsonl co 3 truong:
Truong
Nguon
question
question
solution
deepseek_thinking_trajectory (long-CoT trace cua R1)
answer
\\boxed{...} cuoi cung trong trace, fallback ve solution cua s1K
Giu 934 / 1000 mau — bo cac mau khong co trace, khong co dap an, hoac dap an dai hon 200 ky tu.
from… See the full description on the dataset page: https://huggingface.co/datasets/baesad/s1K-1.1-deepseek-cot.RAIF-ComplexInstruction-DeepSeekThis dataset belongs to the official implementation of the paper "Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models".
Existing large language models (LLMs) face challenges of following complex instructions, especially when multiple constraints are present and organized in paralleling, chaining, and branching structures. One intuitive solution, namely chain-of-thought (CoT), is expected to universally improve capabilities of LLMs. However, we find that the… See the full description on the dataset page: https://huggingface.co/datasets/yolay/RAIF-ComplexInstruction-DeepSeek.tau2-airline-deepseek-distill
τ²-bench airline · DeepSeek teacher trajectories
Successful multi-turn agent trajectories on τ²-bench's
airline domain, generated by running DeepSeek V4 Flash as the agent through the real τ²-bench
harness — same system prompt, same 14 tool schemas, same dialogue loop, same evaluator.
Used to behavior-clone the RL warm start
yuyu0529nya/qwen2.5-7b-tau2-airline-sft-lora,
which is the step 0 of the tau2_airline verl recipe.
Why these exist
GRPO on τ²-bench-airline… See the full description on the dataset page: https://huggingface.co/datasets/yuyu0529nya/tau2-airline-deepseek-distill.DeepSeek-V4-Distill-8000x
🐳 DeepSeek-V4-Distill-8100x
Dataset Summary
DeepSeek-V4-Distill-8100x is a supervised fine-tuning dataset for reasoning-oriented distillation. The question prompts come from Jackrong/GLM-5.1-Reasoning-1M-Cleaned, and the answers were generated by the teacher model DeepSeek-V4-Flash.
After the cleaning process, the released train split contains 7,716 high-quality JSONL examples.
[!NOTE]
The answer pool was cleaned to remove real-time questions… See the full description on the dataset page: https://huggingface.co/datasets/rampisipati/DeepSeek-V4-Distill-8000x.llm_timeline_deepseek_v4_flash-pi
Coding agent session traces
This dataset contains coding agent session traces collected while working on LLM Timeline web app using the prompt from coding-agent-bench-prompts
hwtcm-deepseek-r1-distill-data
简介
DeepSeek蒸馏的传统中医数据集，原始数据来源于网络，未进行人工审查。
7B模型微调效果
模型表现出了推理能力，准确性有待继续验证。
我们的其他产品
中医NER：能识别方剂、本草、来源、病名、症状、证型，也许是基于BERT开源模型中识别最好的模型。中医考试题：也许是全网最早开源、数据最多的中医考试题，我们内部将其用于模型训练的性能评测数据集。中医SFT数据集：中医QA数据集，用于SFT微调。仓公：基于Qwen的指令微调模型（暂未开源）。仓公R1：基于DeepSeek蒸馏的超过100万条QA的指令微调模型，拥有强大的推理能力（暂未开源）。
。。。还有很多
Citation
If you find this project useful in your research, please consider cite:
@misc{hwtcm2024,
title={{hwtcm-deepseek-r1-distill-data} A traditional… See the full description on the dataset page: https://huggingface.co/datasets/Monor/hwtcm-deepseek-r1-distill-data.swebench-deepseek-traces
swebench-deepseek-traces
Complete HTTP-level agentic traces from running swebench_deepseek benchmark tasks through an instrumented reverse proxy.
Each trace captures full request/response pairs including system prompts, user messages, assistant responses, tool calls and results, and token usage metadata.
Stats
Total sessions: 50
Multi-turn sessions (2+ LLM calls): 18
Total records: 542
Total LLM requests: 271
Format
Raw JSONL traces from the instrumented… See the full description on the dataset page: https://huggingface.co/datasets/sammshen/swebench-deepseek-traces.swebench-deepseek-traces-3
swebench-deepseek-traces-3
Complete HTTP-level agentic traces from running swebench_deepseek benchmark tasks through an instrumented reverse proxy.
Each trace captures full request/response pairs including system prompts, user messages, assistant responses, tool calls and results, and token usage metadata.
Stats
Total sessions: 46
Multi-turn sessions (2+ LLM calls): 26
Total records: 2154
Total LLM requests: 1077
Format
Raw JSONL traces from the instrumented… See the full description on the dataset page: https://huggingface.co/datasets/sammshen/swebench-deepseek-traces-3.Deepseek-V4-Flash-11000x
Sherlock Thinking Alpha DeepSeek V4 Flash Distillation
Seed Prompt Dataset
Prompts are sourced from TeichAI/sherlock-thinking-alpha-11000x.
Model
Solutions and reasoning traces were generated with deepseek-ai/DeepSeek-V4-Flash.
DeepSeek-V4-Flash is part of the DeepSeek-V4 preview series. Its model card describes it as a Mixture-of-Experts language model with 284B total parameters, 13B activated parameters, and a 1M-token context length. The model repository is… See the full description on the dataset page: https://huggingface.co/datasets/SLoonker/Deepseek-V4-Flash-11000x.NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54
NOESIS DORA SFT Dataset
Multilingual supervised fine-tuning dataset built for the NOESIS QwQ+DeepSeek-R1 MoE pipeline.
Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform(framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).
Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @djbionicl
NOESIS version: v14.8-NT89
Build date: 2026-04… See the full description on the dataset page: https://huggingface.co/datasets/SMH-DEV-AI/NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54.deepseek-v4-flash-swe-cot
DeepSeek-V4-Flash SWE Agent Trajectories (with raw chain-of-thought)
795 multi-turn software-engineering agent trajectories generated by
DeepSeek-V4-Flash-0731 at reasoning_effort=max, each one executed in a real
repository inside an isolated container and verified by running the repository's own
tests. 469 are verified-correct.
Every assistant turn preserves reasoning_content — the model's raw chain-of-thought,
not a summary. That is the point of this dataset: the DeepSeek API… See the full description on the dataset page: https://huggingface.co/datasets/blythet/deepseek-v4-flash-swe-cot.deepseek-v4-distill-8k
🐳 DeepSeek-V4-Distill-8100x
Dataset Summary
DeepSeek-V4-Distill-8100x is a supervised fine-tuning dataset for reasoning-oriented distillation. The question prompts come from Jackrong/GLM-5.1-Reasoning-1M-Cleaned, and the answers were generated by the teacher model DeepSeek-V4-Flash.
After the cleaning process, the released train split contains 7,716 high-quality JSONL examples.
[!NOTE]
The answer pool was cleaned to remove real-time questions… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/deepseek-v4-distill-8k.