Overview

  • Founded Date October 27, 1976
  • Sectors Health Care
  • Posted Jobs 0
  • Viewed 10
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Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall parameters with 37B triggered for each token. To accomplish efficient inference and affordable training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly verified in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free technique for load balancing and sets a multi-token forecast training objective for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its capabilities. Comprehensive examinations reveal that DeepSeek-V3 surpasses other open-source designs and attains performance similar to leading closed-source designs. Despite its exceptional efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training process is incredibly steady. Throughout the entire 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 efficient architecture of DeepSeek-V2, we leader an auxiliary-loss-free strategy for load balancing, which reduces the efficiency destruction that emerges from motivating load balancing.
– We investigate a Multi-Token Prediction (MTP) goal and prove it helpful to design performance. It can also be utilized for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 mixed precision training framework and, for the first time, validate the expediency and efficiency of FP8 training on a very large-scale model.
– Through co-design of algorithms, frameworks, and hardware, we overcome the interaction bottleneck in cross-node MoE training, almost accomplishing full computation-communication overlap.
This significantly boosts our training performance and reduces the training expenses, allowing us to even more scale up the model size without extra overhead.
– At a cost-effective cost 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 design. The subsequent training phases after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an innovative methodology to distill thinking abilities from the long-Chain-of-Thought (CoT) design, specifically from among the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly includes the confirmation and reflection patterns of R1 into DeepSeek-V3 and especially improves its reasoning efficiency. Meanwhile, we also keep a control over the output style 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 guarantee optimal efficiency and versatility, we have actually partnered with open-source communities and hardware suppliers to supply numerous methods to run the model in your area. For detailed assistance, have a look at Section 6: How_to Run_Locally.

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

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are displayed in vibrant. Scores with a space not going beyond 0.3 are considered to be at the exact same level. DeepSeek-V3 accomplishes the best efficiency on many standards, particularly on mathematics and code jobs. For more assessment 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 larger than 67B)

All models are assessed in a setup that limits the output length to 8K. Benchmarks including less than 1000 samples are checked several times using differing temperature level settings to obtain robust results. DeepSeek-V3 stands as the best-performing open-source design, and also shows 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 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 locally utilizing the following hardware and open-source community software application:

DeepSeek-Infer Demo: We supply an easy and lightweight 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 soon.
LMDeploy: Enables effective FP8 and BF16 reasoning for local and cloud implementation.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
vLLM: Support DeepSeek-V3 design 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 only provide FP8 weights. If you require BF16 weights for experimentation, you can utilize the supplied conversion script to carry out the change.

Here is an example of transforming FP8 weights to BF16:

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

6.1 Inference with DeepSeek-Infer Demo (example just)

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 dependencies listed in requirements.txt. Easiest way is to use a plan manager like conda or uv to develop a new virtual environment and install the dependences.

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 particular format:

Run

Then you can chat with DeepSeek-V3:

Or batch inference on a provided file:

6.2 Inference with SGLang (recommended)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing state-of-the-art latency and throughput performance amongst 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 service.

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

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

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

6.3 Inference with LMDeploy (suggested)

LMDeploy, a flexible and high-performance reasoning and serving structure tailored for big models, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation capabilities, effortlessly integrating with PyTorch-based workflows.

For comprehensive step-by-step instructions 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 model, providing accuracy alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released quickly. You can access the customized branch of TRTLLM specifically for DeepSeek-V3 assistance through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (suggested)

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic strategies, vLLM uses pipeline parallelism permitting you to run this design on several makers connected by networks. For comprehensive assistance, please refer to the vLLM directions. Please feel totally free to follow the enhancement strategy as well.

6.6 Recommended Inference Functionality with AMD GPUs

In cooperation with the AMD team, we have actually attained Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For comprehensive guidance, please describe the SGLang directions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend community has effectively adapted the BF16 variation of DeepSeek-V3. For detailed guidance on Ascend NPUs, please follow the instructions 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 commercial use.

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