DeepSeek-V3 README

The project's README from github.com/deepseek-ai/DeepSeek-V3. As one markdown file: README.md.

DeepSeek-V3
Homepage Chat Hugging Face
Discord Wechat Twitter Follow
Code License Model License
Paper Link👁️

Table of Contents

  1. Introduction
  2. Model Summary
  3. Model Downloads
  4. Evaluation Results
  5. Chat Website & API Platform
  6. How to Run Locally
  7. License
  8. Citation
  9. Contact

1. Introduction

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.

2. Model Summary

Architecture: Innovative Load Balancing Strategy and Training Objective

Pre-Training: Towards Ultimate Training Efficiency

Post-Training: Knowledge Distillation from DeepSeek-R1

3. Model Downloads

Model #Total Params #Activated Params Context Length Download
DeepSeek-V3-Base 671B 37B 128K 🤗 Hugging Face
DeepSeek-V3 671B 37B 128K 🤗 Hugging Face

[!NOTE] The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.

To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: How_to Run_Locally.

For developers looking to dive deeper, we recommend exploring README_WEIGHTS.md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Benchmark (Metric) # Shots DeepSeek-V2 Qwen2.5 72B LLaMA3.1 405B DeepSeek-V3
Architecture - MoE Dense Dense MoE
# Activated Params - 21B 72B 405B 37B
# Total Params - 236B 72B 405B 671B
English Pile-test (BPB) - 0.606 0.638 0.542 0.548
BBH (EM) 3-shot 78.8 79.8 82.9 87.5
MMLU (Acc.) 5-shot 78.4 85.0 84.4 87.1
MMLU-Redux (Acc.) 5-shot 75.6 83.2 81.3 86.2
MMLU-Pro (Acc.) 5-shot 51.4 58.3 52.8 64.4
DROP (F1) 3-shot 80.4 80.6 86.0 89.0
ARC-Easy (Acc.) 25-shot 97.6 98.4 98.4 98.9
ARC-Challenge (Acc.) 25-shot 92.2 94.5 95.3 95.3
HellaSwag (Acc.) 10-shot 87.1 84.8 89.2 88.9
PIQA (Acc.) 0-shot 83.9 82.6 85.9 84.7
WinoGrande (Acc.) 5-shot 86.3 82.3 85.2 84.9
RACE-Middle (Acc.) 5-shot 73.1 68.1 74.2 67.1
RACE-High (Acc.) 5-shot 52.6 50.3 56.8 51.3
TriviaQA (EM) 5-shot 80.0 71.9 82.7 82.9
NaturalQuestions (EM) 5-shot 38.6 33.2 41.5 40.0
AGIEval (Acc.) 0-shot 57.5 75.8 60.6 79.6
Code HumanEval (Pass@1) 0-shot 43.3 53.0 54.9 65.2
MBPP (Pass@1) 3-shot 65.0 72.6 68.4 75.4
LiveCodeBench-Base (Pass@1) 3-shot 11.6 12.9 15.5 19.4
CRUXEval-I (Acc.) 2-shot 52.5 59.1 58.5 67.3
CRUXEval-O (Acc.) 2-shot 49.8 59.9 59.9 69.8
Math GSM8K (EM) 8-shot 81.6 88.3 83.5 89.3
MATH (EM) 4-shot 43.4 54.4 49.0 61.6
MGSM (EM) 8-shot 63.6 76.2 69.9 79.8
CMath (EM) 3-shot 78.7 84.5 77.3 90.7
Chinese CLUEWSC (EM) 5-shot 82.0 82.5 83.0 82.7
C-Eval (Acc.) 5-shot 81.4 89.2 72.5 90.1
CMMLU (Acc.) 5-shot 84.0 89.5 73.7 88.8
CMRC (EM) 1-shot 77.4 75.8 76.0 76.3
C3 (Acc.) 0-shot 77.4 76.7 79.7 78.6
CCPM (Acc.) 0-shot 93.0 88.5 78.6 92.0
Multilingual MMMLU-non-English (Acc.) 5-shot 64.0 74.8 73.8 79.4

[!NOTE] Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks. For more evaluation details, please check our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to 128K.

Chat Model

Standard Benchmarks (Models larger than 67B)

Benchmark (Metric) DeepSeek V2-0506 DeepSeek V2.5-0905 Qwen2.5 72B-Inst. Llama3.1 405B-Inst. Claude-3.5-Sonnet-1022 GPT-4o 0513 DeepSeek V3
Architecture MoE MoE Dense Dense - - MoE
# Activated Params 21B 21B 72B 405B - - 37B
# Total Params 236B 236B 72B 405B - - 671B
English MMLU (EM) 78.2 80.6 85.3 88.6 88.3 87.2 88.5
MMLU-Redux (EM) 77.9 80.3 85.6 86.2 88.9 88.0 89.1
MMLU-Pro (EM) 58.5 66.2 71.6 73.3 78.0 72.6 75.9
DROP (3-shot F1) 83.0 87.8 76.7 88.7 88.3 83.7 91.6
IF-Eval (Prompt Strict) 57.7 80.6 84.1 86.0 86.5 84.3 86.1
GPQA-Diamond (Pass@1) 35.3 41.3 49.0 51.1 65.0 49.9 59.1
SimpleQA (Correct) 9.0 10.2 9.1 17.1 28.4 38.2 24.9
FRAMES (Acc.) 66.9 65.4 69.8 70.0 72.5 80.5 73.3
LongBench v2 (Acc.) 31.6 35.4 39.4 36.1 41.0 48.1 48.7
Code HumanEval-Mul (Pass@1) 69.3 77.4 77.3 77.2 81.7 80.5 82.6
LiveCodeBench (Pass@1-COT) 18.8 29.2 31.1 28.4 36.3 33.4 40.5
LiveCodeBench (Pass@1) 20.3 28.4 28.7 30.1 32.8 34.2 37.6
Codeforces (Percentile) 17.5 35.6 24.8 25.3 20.3 23.6 51.6
SWE Verified (Resolved) - 22.6 23.8 24.5 50.8 38.8 42.0
Aider-Edit (Acc.) 60.3 71.6 65.4 63.9 84.2 72.9 79.7
Aider-Polyglot (Acc.) - 18.2 7.6 5.8 45.3 16.0 49.6
Math AIME 2024 (Pass@1) 4.6 16.7 23.3 23.3 16.0 9.3 39.2
MATH-500 (EM) 56.3 74.7 80.0 73.8 78.3 74.6 90.2
CNMO 2024 (Pass@1) 2.8 10.8 15.9 6.8 13.1 10.8 43.2
Chinese CLUEWSC (EM) 89.9 90.4 91.4 84.7 85.4 87.9 90.9
C-Eval (EM) 78.6 79.5 86.1 61.5 76.7 76.0 86.5
C-SimpleQA (Correct) 48.5 54.1 48.4 50.4 51.3 59.3 64.8

[!NOTE] All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models.

Open Ended Generation Evaluation

Model Arena-Hard AlpacaEval 2.0
DeepSeek-V2.5-0905 76.2 50.5
Qwen2.5-72B-Instruct 81.2 49.1
LLaMA-3.1 405B 69.3 40.5
GPT-4o-0513 80.4 51.1
Claude-Sonnet-3.5-1022 85.2 52.0
DeepSeek-V3 85.5 70.0

[!NOTE] English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can chat with DeepSeek-V3 on DeepSeek's official website: chat.deepseek.com

We also provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be deployed locally using the following hardware and open-source community software:

  1. DeepSeek-Infer Demo: We provide a simple and lightweight demo for FP8 and BF16 inference.
  2. SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
  3. LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud deployment.
  4. TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
  5. vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
  6. LightLLM: Supports efficient single-node or multi-node deployment for FP8 and BF16.
  7. AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
  8. Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices in both INT8 and BF16.

Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation.

Here is an example of converting FP8 weights to BF16:

cd inference
python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights

[!NOTE] Hugging Face's Transformers has not been directly supported yet.

6.1 Inference with DeepSeek-Infer Demo (example only)

System Requirements

[!NOTE] Linux with Python 3.10 only. Mac and Windows are not supported.

Dependencies:

torch==2.4.1
triton==3.0.0
transformers==4.46.3
safetensors==0.4.5

Model Weights & Demo Code Preparation

First, clone our DeepSeek-V3 GitHub repository:

git clone https://github.com/deepseek-ai/DeepSeek-V3.git

Navigate to the inference folder and install dependencies listed in requirements.txt. Easiest way is to use a package manager like conda or uv to create a new virtual environment and install the dependencies.

cd DeepSeek-V3/inference
pip install -r requirements.txt

Download the model weights from Hugging Face, and put them into /path/to/DeepSeek-V3 folder.

Model Weights Conversion

Convert Hugging Face model weights to a specific format:

python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16

Run

Then you can chat with DeepSeek-V3:

torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200

Or batch inference on a given file:

torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE

SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks.

Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust solution.

SGLang also supports multi-node tensor parallelism, enabling you to run this model on multiple network-connected machines.

Multi-Token Prediction (MTP) is in development, and progress can be tracked in the optimization plan.

Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3

LMDeploy, a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows.

For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960

TensorRT-LLM now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/deepseek_v3.

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers pipeline parallelism allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the vLLM instructions. Please feel free to follow the enhancement plan as well.

LightLLM v1.0.1 supports single-machine and multi-machine tensor parallel deployment for DeepSeek-R1 (FP8/BF16) and provides mixed-precision deployment, with more quantization modes continuously integrated. For more details, please refer to LightLLM instructions. Additionally, LightLLM offers PD-disaggregation deployment for DeepSeek-V2, and the implementation of PD-disaggregation for DeepSeek-V3 is in development.

In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the SGLang instructions.

The MindIE framework from the Huawei Ascend community has s

DeepSeek-V3 (deepseek-ai/DeepSeek-V3)

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DeepSeek-V3
这是什么？DeepSeek-V3 是一个开源权重的混合专家（MoE）语言模型，总参数量为 671B，但每个词元仅激活 37B 参数。它专为高效且高性能的推理而设计，在许多基准测试上可与领先的闭源模型相媲美。
这是训练好的模型发布，还是训练框架？这是一套已发布的模型权重（基础版和对话版），外加一个轻量的推理演示。重点不在训练代码，你应该使用 vLLM 或 SGLang 等社区工具来进行推理。
我该怎么在本地运行？克隆 DeepSeek-V3 仓库，进入 inference 文件夹，安装 requirements.txt 中的依赖（推荐使用 conda 或 uv），然后从 Hugging Face 下载 FP8 权重到 DeepSeek-V3 文件夹，并运行 DeepSeek-Infer 演示。SGLang、LMDeploy、TensorRT-LLM、vLLM 和 LightLLM 也都支持。
运行它需要什么硬件和软件？你需要一台 Linux 机器，安装 Python 3.10，并配备足够多的 GPU 来容纳 671B 参数的模型（原始训练使用的是 H800 GPU）。也可以通过 SGLang 使用 AMD GPU，或使用华为昇腾 NPU 作为替代方案。
让权重跑起来时有什么需要注意的坑吗？由于训练使用了 FP8，原生只提供 FP8 权重。如果需要 BF16 权重，就要用转换脚本。另外，Hugging Face Transformers 目前还不能直接支持。
DeepSeek-V3 的独特之处在于哪项架构技巧？它在 MoE 层中采用无辅助损失的负载均衡策略，并结合多词元预测（MTP）训练目标。后者还能通过投机解码加速推理。
训练到底花了多少钱？在 14.8 万亿个词元上进行预训练，仅耗时 266.4 万 H800 GPU 小时。后训练阶段又额外增加了 10 万 GPU 小时，总计约 278.8 万 H800 GPU 小时。
我不自己运行也能试用吗？可以。你可以在 chat.deepseek.com 上与 DeepSeek-V3 对话，也可以在 platform.deepseek.com 使用兼容 OpenAI 的 API。
对话模型的推理能力是怎么来的？DeepSeek-V3 在后训练阶段，把长思维链模型 DeepSeek-R1 的推理模式蒸馏到了自身中。这提升了推理能力，同时也保持了输出长度和风格的可控。
What are similar projects?Hy4-preview, StartLux-Decision, DeepSeek-R1, Qwen3.8, Kimi-K3, Hy-MT2, Kimi-K2, ktransformers
Which topics is it in?open-weights, amd-gpu, mixture-of-experts, reasoning, moe, vllm, quantization, openai-compatible, transformer, large-language-models, artificial-intelligence, chatbot
Creator
deepseek-ai
Size
1.7 MB
Stars
104.5K
Updated
CommitAug 28Releasev1.0.0Jun 27
Age
1y
Commits · 90 Days