datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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-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-Distill-Llama-8B-lmsys-openthoughts-tokenizedThis dataset is used for training Sparse Autoencoders (SAEs) to identify reasoning features in Large Language Models (LLMs), as described in the paper I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders.
Code for the paper is available at: https://github.com/AIRI-Institute/SAE-Reasoning
The dataset consists of tokenized text data used for training the SAEs.
dataset_info:
features:
name: tokens
sequence: int64
splits:
name:… See the full description on the dataset page: https://huggingface.co/datasets/andreuka18/DeepSeek-R1-Distill-Llama-8B-lmsys-openthoughts-tokenized.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-Distilled-1.4MFor more open-source datasets, models, and methodologies, please visit our GitHub repository.
AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of
high-quality and challenging reasoning problems. These problems are collected from numerous
open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination.
All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone
rigorous… See the full description on the dataset page: https://huggingface.co/datasets/ibndias/DeepSeek-R1-Distilled-1.4M.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-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-Distill-Llama-8B-MATH-traces
DeepSeek-R1-Distill-Llama-8B MATH Reasoning Traces
10,000 reasoning traces from DeepSeek-R1-Distill-Llama-8B on MATH problems.
Model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B (served via vLLM)
Source problems: xDAN2099/lighteval-MATH (train split)
Sampling: 500 problems (100 per difficulty level 1-5) x 20 rollouts
Generation params: temperature=0.6, top_p=0.95, max_tokens=15000
Accuracy: 80.1% (8,008 correct / 1,992 incorrect)
Problem types: Algebra, Counting & Probability… See the full description on the dataset page: https://huggingface.co/datasets/jrosseruk/DeepSeek-R1-Distill-Llama-8B-MATH-traces.DeepSeek-R1-Distill-Llama-8B-MATH-labeled-sentences
DeepSeek-R1-Distill-Llama-8B MATH Labeled Sentences
Sentence-level function-tag labels for reasoning traces from DeepSeek-R1-Distill-Llama-8B on MATH problems.
Trace model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B (served via vLLM)
Label model: gpt-4o-mini
Source traces: jrosseruk/DeepSeek-R1-Distill-Llama-8B-MATH-traces-balanced
Total sentences: 435,525
Backtrack sentences: 39,998 (9.2%)
Traces: 4,413 (2,496 correct, 1,917 incorrect)
Each sentence in a chain-of-thought trace is… See the full description on the dataset page: https://huggingface.co/datasets/jrosseruk/DeepSeek-R1-Distill-Llama-8B-MATH-labeled-sentences.DeepSeek-R1-Distilled-Translate-en-zh_CN-39k-Alpaca-GPT4-without-Think
DeepSeek R1 满血蒸馏英中翻译数据集 Alpaca GPT-4（无 CoT 版本）
本数据集是 @FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k 的 Alpaca GPT-4 简化版本，专门用于微调语言模型的英中翻译任务。主要区别在于移除了原数据集中的思考过程（Chain of Thought，CoT），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 #… See the full description on the dataset page: https://huggingface.co/datasets/FradSer/DeepSeek-R1-Distilled-Translate-en-zh_CN-39k-Alpaca-GPT4-without-Think.DeepSeek-R1-Distill-Llama-8B-MATH-traces-balanced
DeepSeek-R1-Distill-Llama-8B MATH Reasoning Traces (Balanced)
4,492 reasoning traces from DeepSeek-R1-Distill-Llama-8B on MATH problems, balanced for correct/incorrect.
Model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B (served via vLLM)
Source problems: xDAN2099/lighteval-MATH (train split)
Sampling: Subsampled from the full 10k trace set — 2,500 correct + 1,992 incorrect (all available incorrect traces)
Generation params: temperature=0.6, top_p=0.95, max_tokens=15000
Accuracy: 55.7%… See the full description on the dataset page: https://huggingface.co/datasets/jrosseruk/DeepSeek-R1-Distill-Llama-8B-MATH-traces-balanced.DeepSeek-R1_Distilldeepseek-r1-distill-llama-70b-synthetic
Dataset Card for Dataset Name
This dataset contains input and output pairs from the popular Deepseek-R1 model, distilled into Llama-70B.
Dataset Details
Curated by: ReflexAI
Language(s) (NLP): English
License: Llama3.3
If you like the open source work of ReflexAI, don't hesitate to give us a follow on huggingface, or like this dataset.