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GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B total criteria with 37B activated for each token. To accomplish efficient inference and economical training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely confirmed 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 efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its abilities. Comprehensive examinations expose that DeepSeek-V3 outperforms other open-source designs and similar to leading closed-source designs. Despite its outstanding efficiency, DeepSeek-V3 requires just 2.788 M H800 GPU hours for its complete training. In addition, its training process is remarkably stable. Throughout the whole training procedure, we did not experience any irrecoverable loss spikes or perform any rollbacks.

2. Model Summary

Architecture: Innovative Load Balancing Strategy and Training Objective

– On top of the effective architecture of DeepSeek-V2, we leader an auxiliary-loss-free method for load balancing, which reduces the efficiency deterioration that occurs from motivating load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model efficiency. It can likewise be utilized for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 blended precision training structure and, for the very first time, verify the expediency and efficiency of FP8 training on an incredibly massive model.
– Through co-design of algorithms, structures, and hardware, we conquer the communication traffic jam in cross-node MoE training, almost achieving complete computation-communication overlap.
This significantly improves our training effectiveness and minimizes the training expenses, enabling us to further scale up the design size without additional overhead.
– At a cost-effective cost of only 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently greatest open-source base design. The subsequent training stages after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an ingenious method to distill reasoning abilities from the long-Chain-of-Thought (CoT) design, particularly from among the DeepSeek R1 series models, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly integrates the verification and reflection patterns of R1 into DeepSeek-V3 and especially enhances its thinking efficiency. Meanwhile, we also maintain a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The overall size of DeepSeek-V3 models on Hugging Face is 685B, which consists of 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To ensure optimal efficiency and flexibility, we have actually partnered with open-source neighborhoods and hardware suppliers to offer numerous ways to run the model locally. For detailed assistance, have a look at Section 6: How_to Run_Locally.

For designers wanting to dive much deeper, we advise checking out README_WEIGHTS. md for information 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 invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are displayed in vibrant. 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 mathematics and code jobs. For more examination details, please check our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 carries out well across all context window lengths as much as 128K.

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are examined in a configuration that restricts the output length to 8K. Benchmarks including fewer than 1000 samples are checked numerous times utilizing differing temperature settings to obtain robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise exhibits competitive performance versus frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended conversation evaluations. For AlpacaEval 2.0, we utilize the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can chat with DeepSeek-V3 on DeepSeek’s official site: chat.deepseek.com

We likewise supply OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be released in your area utilizing the following hardware and open-source community software application:

DeepSeek-Infer Demo: We provide a basic and light-weight demonstration for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 reasoning for local and cloud deployment.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming quickly.
vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 design on AMD GPUs via SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices.
Since FP8 training is natively embraced in our structure, we just supply FP8 weights. If you need BF16 weights for experimentation, you can utilize the provided conversion script to carry out the transformation.

Here is an example of converting FP8 weights to BF16:

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 just. Mac and Windows are not supported.

Dependencies:

Model Weights & Demo Code Preparation

First, clone our DeepSeek-V3 GitHub repository:

Navigate to the reasoning folder and install dependencies listed in requirements.txt. Easiest method is to use a plan supervisor like conda or uv to create a brand-new virtual environment and install the dependences.

Download the design weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.

Model Weights Conversion

Convert Hugging Face design weights to a specific format:

Run

Then you can chat with DeepSeek-V3:

Or batch inference on a given file:

6.2 Inference with SGLang (suggested)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing advanced latency and throughput performance amongst open-source frameworks.

Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly flexible and robust option.

SGLang likewise supports multi-node tensor parallelism, allowing you to run this model on several network-connected machines.

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

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

6.3 Inference with LMDeploy (recommended)

LMDeploy, a versatile and high-performance reasoning and serving structure customized for big language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online release capabilities, seamlessly integrating with PyTorch-based workflows.

For detailed step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (suggested)

TensorRT-LLM now supports the DeepSeek-V3 design, using precision choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in progress and will be launched quickly. You can access the custom-made branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the brand-new functions straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (advised)

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM provides pipeline parallelism allowing you to run this design on multiple devices connected by networks. For comprehensive assistance, please refer to the vLLM instructions. Please do not hesitate to follow the improvement strategy as well.

6.6 Recommended Inference Functionality with AMD GPUs

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

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend neighborhood has effectively adapted the BF16 version of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the directions here.

7. License

This code repository is licensed under the MIT License. The usage of DeepSeek-V3 Base/Chat designs undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports industrial use.