Name: DeepSeek R1
Author: deepseek-ai

简介

DeepSeek R1 是一款主打强化学习（RL）的推理模型，旨在对标 OpenAI o1。它最核心的突破在于通过大规模强化学习提升了逻辑推理、数学证明和代码编写能力，能够通过“慢思考”生成详细的思维链（CoT），让用户清晰看到模型的推理路径。对于国内开发者而言，R1 的最大亮点在于其极高的性价比和开源属性，它不仅在复杂逻辑任务上表现强劲，且上手门槛低，可通过 API 或本地部署快速集成到现有工作流中，是替代昂贵闭源推理模型的理想选择。

核心亮点

强大的逻辑推理与复杂代码编写能力
支持透明的思维链输出，推理过程可见
基于 MIT 协议开源，部署与集成成本极低
在数学和编程等硬核指标上比肩顶尖模型

使用方法

安装依赖

# 安装 Hugging Face transformers
pip install transformers torch

SDK 使用

# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("deepseek-ai/DeepSeek-R1")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引

pip install -U huggingface_hub

命令行下载

下载完整模型库

huggingface-cli download deepseek-ai/DeepSeek-R1

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

huggingface-cli download deepseek-ai/DeepSeek-R1 config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('deepseek-ai/DeepSeek-R1')

Git 下载

请确保 lfs 已经被正确安装

Git 下载

git lfs install
git clone https://huggingface.co/deepseek-ai/DeepSeek-R1

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-R1

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

pip install -U transformers torch

模型加载和推理

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('deepseek-ai/DeepSeek-R1')
tokenizer = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-R1')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引

pip install modelscope

命令行下载

下载完整模型库

modelscope download --model deepseek-ai/DeepSeek-R1

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

modelscope download --model deepseek-ai/DeepSeek-R1 README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('deepseek-ai/DeepSeek-R1')

Git 下载

请确保 lfs 已经被正确安装

Git 下载

git lfs install
git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-R1.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-R1.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'deepseek-ai/DeepSeek-R1')

完整文档

来源: HuggingFace

---
license: mit
library_name: transformers
---

DeepSeek-R1

<p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>👁️</a>
</p>

1. Introduction

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
we introduce DeepSeek-R1, which incorporates cold-start data before RL.
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.

2. Model Summary

---

Post-Training: Large-Scale Reinforcement Learning on the Base Model

We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.

We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.

We believe the pipeline will benefit the industry by creating better models.

---

Distillation: Smaller Models Can Be Powerful Too

We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future.

Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.

3. Model Downloads

DeepSeek-R1 Models

| Model | #Total Params | #Activated Params | Context Length | Download |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| DeepSeek-R1-Zero | 671B | 37B | 128K | 🤗 HuggingFace |
| DeepSeek-R1 | 671B | 37B | 128K | 🤗 HuggingFace |

</div>

DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base.
For more details regarding the model architecture, please refer to DeepSeek-V3 repository.

DeepSeek-R1-Distill Models

</div>

DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
We slightly change their configs and tokenizers. Please use our setting to run these models.

4. Evaluation Results

DeepSeek-R1-Evaluation

For all our models, the maximum gene

DeepSeek R1

简介

核心亮点

使用方法

Hugging Face 下载

命令行下载

SDK 下载

Git 下载

PyTorch / Transformers 使用

模型下载

命令行下载

SDK 下载

Git 下载

Notebook 快速开发

完整文档

DeepSeek-R1

1. Introduction

2. Model Summary

3. Model Downloads

DeepSeek-R1 Models

DeepSeek-R1-Distill Models

4. Evaluation Results

DeepSeek-R1-Evaluation

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