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Overview

  • Founded Date July 23, 1954
  • Sectors تركيبات
  • Posted Jobs 0
  • Viewed 6

Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B total specifications with 37B triggered 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 method for load balancing and sets a multi-token forecast training goal for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations expose that DeepSeek-V3 outshines other open-source designs and accomplishes efficiency similar to leading closed-source models. Despite its performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is remarkably steady. Throughout the whole training process, we did not experience any irrecoverable loss spikes or carry out any rollbacks.

2. Model Summary

Architecture: Innovative Load Balancing Strategy and Training Objective

– On top of the effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which decreases the efficiency deterioration that develops from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) objective and prove it beneficial to design efficiency. It can also be used for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 combined precision training structure and, for the very first time, validate the expediency and effectiveness of FP8 training on an extremely large-scale design.
– Through co-design of algorithms, structures, and hardware, we get rid of the communication bottleneck in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This considerably boosts our training effectiveness and decreases the training costs, enabling us to even more scale up the design size without extra overhead.
– At an economical expense of just 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training need only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an innovative methodology to boil down thinking abilities from the long-Chain-of-Thought (CoT) model, particularly from one of the DeepSeek R1 series designs, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly includes the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning efficiency. Meanwhile, we also preserve 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 includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To make sure ideal efficiency and flexibility, we have partnered with open-source communities and hardware vendors to offer multiple ways to run the design in your area. For detailed assistance, have a look at Section 6: How_to Run_Locally.

For developers wanting to dive deeper, we suggest checking out 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 advancement within the neighborhood, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are revealed in vibrant. Scores with a space not going beyond 0.3 are thought about to be at the same level. DeepSeek-V3 attains the very best efficiency on most criteria, specifically on math and code tasks. For more examination information, please check our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are evaluated in a setup that limits the output length to 8K. Benchmarks containing less than 1000 samples are checked several times utilizing varying temperature settings to obtain robust results. DeepSeek-V3 stands as the best-performing open-source model, and likewise exhibits competitive performance against frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended conversation examinations. 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 main website: 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 locally using the following hardware and open-source community software:

DeepSeek-Infer Demo: We offer a basic and light-weight demonstration for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 reasoning for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 assistance 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 model 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 adopted in our framework, we only supply FP8 weights. If you require BF16 weights for experimentation, you can utilize the supplied conversion script to perform the improvement.

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 only. Mac and Windows are not supported.

Dependencies:

Model Weights & Demo Code Preparation

First, clone our DeepSeek-V3 GitHub repository:

Navigate to the inference folder and install dependences listed in requirements.txt. Easiest way is to use a bundle supervisor like conda or uv to produce a brand-new virtual environment and set up the reliances.

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

Model Weights Conversion

Convert Hugging Face model weights to a specific format:

Run

Then you can talk with DeepSeek-V3:

Or batch reasoning on an offered file:

6.2 Inference with SGLang (advised)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering cutting edge latency and throughput performance among open-source frameworks.

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

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

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

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 (advised)

LMDeploy, a flexible and high-performance inference and serving framework customized for big language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online release abilities, perfectly integrating with PyTorch-based workflows.

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

6.4 Inference with TRT-LLM (advised)

TensorRT-LLM now supports the DeepSeek-V3 design, providing precision choices 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 particularly for DeepSeek-V3 assistance 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 model on multiple devices connected by networks. For detailed guidance, please refer to the vLLM directions. Please feel complimentary to follow the enhancement strategy too.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have actually attained Day-One support for AMD GPUs utilizing SGLang, with full compatibility for both FP8 and BF16 accuracy. For comprehensive assistance, please refer to the SGLang instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has actually effectively adapted the BF16 variation of DeepSeek-V3. For detailed guidance on Ascend NPUs, please follow the guidelines here.

7. License

This code repository is certified under the MIT License. Using DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports business usage.