Overview

  • Founded Date 13/02/1990
  • Sectors Health Care
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Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To accomplish effective inference and cost-effective training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free technique for load balancing and sets a multi-token forecast training objective for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its abilities. Comprehensive evaluations expose that DeepSeek-V3 surpasses other open-source models and attains performance similar to leading closed-source designs. Despite its outstanding performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training process is remarkably steady. Throughout the entire training procedure, 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 leader an auxiliary-loss-free strategy for load balancing, which lessens the performance degradation that occurs from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) objective and prove it useful to model efficiency. It can also be utilized for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 blended accuracy training structure and, for the very first time, validate the expediency and efficiency of FP8 training on an exceptionally massive model.
– Through co-design of algorithms, structures, and hardware, we conquer the interaction bottleneck in cross-node MoE training, almost attaining full computation-communication overlap.
This considerably improves our training effectiveness and lowers the training costs, allowing us to even more scale up the design size without extra overhead.
– At an economical expense of only 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 design. The subsequent training stages after pre-training need only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We present an innovative approach to distill thinking abilities from the long-Chain-of-Thought (CoT) design, specifically from among the DeepSeek R1 series models, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and significantly improves its thinking efficiency. Meanwhile, we likewise preserve a control over the output design and length of DeepSeek-V3.

3. Model Downloads

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

To make sure optimal efficiency and versatility, we have actually partnered with open-source communities and hardware vendors to supply multiple ways to run the design locally. For step-by-step assistance, take a look at Section 6: How_to Run_Locally.

For designers seeking to dive deeper, we recommend 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 community, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are displayed in vibrant. Scores with a space not surpassing 0.3 are considered to be at the very same level. DeepSeek-V3 accomplishes the finest performance on most criteria, especially on mathematics and code jobs. For more assessment details, please inspect our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are evaluated in a configuration that restricts the output length to 8K. Benchmarks consisting of less than 1000 samples are evaluated multiple times using differing temperature settings to obtain robust last outcomes. DeepSeek-V3 stands as the best-performing open-source design, and also displays competitive performance versus frontier closed-source models.

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 official 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 utilizing the following hardware and open-source community software:

DeepSeek-Infer Demo: We provide a basic and lightweight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables effective FP8 and BF16 reasoning for regional and cloud implementation.
TensorRT-LLM: Currently supports BF16 inference 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 design on AMD GPUs through 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 framework, we only supply FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to carry out the transformation.

Here is an example of transforming FP8 weights to BF16:

Hugging Face’s Transformers has actually not been directly supported yet. **

6.1 Inference with DeepSeek-Infer Demo (example just)

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 inference folder and set up dependencies listed in requirements.txt. Easiest method is to utilize a plan manager like conda or uv to produce a brand-new virtual environment and install the dependencies.

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

Model Weights Conversion

Convert Hugging Face model weights to a particular format:

Run

Then you can talk with DeepSeek-V3:

Or batch reasoning on a provided file:

6.2 Inference with SGLang (suggested)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, 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 an extremely versatile and robust solution.

SGLang also supports multi-node tensor parallelism, enabling you to run this design on numerous network-connected devices.

Multi-Token Prediction (MTP) remains in development, and progress 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 inference and serving structure customized for large language designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online implementation abilities, flawlessly integrating with PyTorch-based workflows.

For thorough detailed directions 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 model, providing precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in development and will be launched soon. You can access the custom-made branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the brand-new features straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (suggested)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic strategies, vLLM provides pipeline parallelism allowing you to run this design on multiple devices linked by networks. For detailed guidance, please describe the vLLM directions. Please feel totally free to follow the enhancement strategy too.

6.6 Recommended Inference Functionality with AMD GPUs

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

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend neighborhood has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step 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 is subject to the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports commercial usage.

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