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
Что это?DeepSeek-V3 — это языковая модель с открытыми весами и архитектурой Mixture-of-Experts (MoE): в ней 671 млрд параметров, но на каждый токен активируется только 37 млрд. Модель создана для эффективного и при этом высокопроизводительного инференса и на многих тестах сопоставима с ведущими закрытыми моделями.
Это релиз обученной модели или фреймворк для обучения?Это опубликованный набор весов модели (базовая и чат-версии) плюс легковесное демо для инференса. Сам код обучения не в центре внимания: предполагается, что модель запускают через инструменты сообщества, например vLLM или SGLang.
Как запустить модель локально?Клонируйте репозиторий DeepSeek-V3, перейдите в папку inference, установите зависимости из requirements.txt (рекомендуются conda или uv), затем скачайте веса FP8 с Hugging Face в папку DeepSeek-V3 и запустите демо DeepSeek-Infer. Также поддерживаются SGLang, LMDeploy, TensorRT-LLM, vLLM и LightLLM.
Какое железо и софт нужны для запуска?Нужна машина на Linux с Python 3.10 и достаточным количеством GPU, чтобы вместить модель на 671 млрд параметров (изначально она обучалась на GPU H800). В качестве альтернатив поддерживаются GPU AMD через SGLang и NPU Huawei Ascend.
Есть ли подводные камни при запуске весов?Нативно предоставляются только веса FP8, так как обучение шло в FP8, поэтому для весов BF16 понадобится скрипт конвертации. Кроме того, библиотека Hugging Face Transformers пока не поддерживается напрямую.
В чём архитектурная фишка, которая делает DeepSeek-V3 особенной?Для слоёв MoE используется стратегия балансировки нагрузки без вспомогательной функции потерь, а также цель обучения Multi-Token Prediction (MTP), которая ещё и ускоряет инференс за счёт спекулятивного декодирования.
Сколько на самом деле стоило обучение?Предобучение на 14,8 трлн токенов заняло всего 2,664 млн часов GPU H800, а этапы постобучения добавили ещё 0,1 млн часов GPU. В сумме получилось около 2,788 млн часов GPU H800.
Можно ли попробовать модель, не запуская её самому?Да, пообщаться с DeepSeek-V3 можно на chat.deepseek.com, а также использовать API, совместимый с OpenAI, на platform.deepseek.com.
Откуда у чат-модели способность к рассуждениям?В ходе постобучения DeepSeek-V3 путём дистилляции переняла шаблоны рассуждений у модели DeepSeek-R1 с длинной цепочкой рассуждений (long-Chain-of-Thought). Это улучшило рассуждения, при этом длина и стиль ответов остаются под контролем.
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