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Overview

  • Founded Date May 4, 1942
  • Sectors Health Care
  • Posted Jobs 0
  • Viewed 5
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Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To attain efficient inference and cost-efficient training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly verified in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free method for load balancing and sets a multi-token prediction training objective for more powerful efficiency. 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 abilities. Comprehensive evaluations expose that DeepSeek-V3 surpasses other open-source designs and accomplishes performance equivalent to leading closed-source designs. Despite its excellent performance, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is remarkably stable. 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 efficient architecture of DeepSeek-V2, we leader an auxiliary-loss-free method for load balancing, which lessens the efficiency deterioration that occurs from motivating load balancing.
– We investigate a Multi-Token Prediction (MTP) goal and prove it useful to model efficiency. It can also be utilized for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 blended accuracy training framework and, for the very first time, validate the expediency and effectiveness of FP8 training on an exceptionally large-scale design.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the communication traffic jam in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This considerably improves our training efficiency and lowers the training costs, enabling us to even more scale up the design size without extra overhead.
– At a cost-effective expense of just 2.664 M H800 GPU hours, we finish 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 require only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an innovative approach to boil down thinking abilities from the long-Chain-of-Thought (CoT) design, particularly from one of the DeepSeek R1 series designs, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly includes the verification and reflection patterns of R1 into DeepSeek-V3 and notably enhances its thinking efficiency. Meanwhile, we likewise maintain a control over the output style and length of DeepSeek-V3.

3. Model Downloads

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

To ensure ideal performance and versatility, we have partnered with open-source neighborhoods and hardware suppliers to provide several methods to run the model in your area. For detailed guidance, take a look at Section 6: How_to Run_Locally.

For designers seeking 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 presently under active development within the community, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are displayed in vibrant. Scores with a gap not surpassing 0.3 are thought about to be at the very same level. DeepSeek-V3 achieves the best performance on the majority of criteria, particularly on math and code tasks. For more examination details, please examine our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are examined in a setup that restricts the output length to 8K. Benchmarks containing less than 1000 samples are evaluated several times utilizing varying temperature level settings to obtain robust results. DeepSeek-V3 stands as the best-performing open-source design, and also displays competitive efficiency against frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended conversation assessments. For AlpacaEval 2.0, we utilize the 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 supply OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

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

DeepSeek-Infer Demo: We supply a basic and light-weight demo 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 efficient FP8 and BF16 reasoning for regional and cloud release.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance 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 model 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 adopted in our framework, we just provide FP8 weights. If you require BF16 weights for experimentation, you can utilize the provided conversion script to perform the change.

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 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 reasoning folder and install dependences noted in requirements.txt. Easiest way is to use a package manager like conda or uv to produce a brand-new virtual environment and install the dependencies.

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 talk with DeepSeek-V3:

Or batch inference 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, providing cutting edge latency and throughput performance amongst open-source structures.

Notably, SGLang v0.4.1 totally 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, allowing you to run this design on multiple network-connected machines.

Multi-Token Prediction (MTP) remains in development, and progress 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 (recommended)

LMDeploy, a flexible and high-performance inference and serving framework tailored for large language designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online implementation abilities, seamlessly integrating with PyTorch-based workflows.

For extensive step-by-step 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 design, offering accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently 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 new functions straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (recommended)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic strategies, vLLM offers pipeline parallelism permitting you to run this model on multiple machines connected by networks. For in-depth guidance, please describe the vLLM directions. Please do not hesitate to follow the improvement strategy also.

6.6 Recommended Inference Functionality with AMD GPUs

In collaboration with the AMD group, we have attained Day-One assistance for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 accuracy. For comprehensive guidance, please refer to the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend neighborhood has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step 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 (including Base and Chat) supports industrial usage.

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