Overview

  • Founded Date May 31, 2002
  • Sectors Data Science
  • Posted Jobs 0
  • Viewed 9

Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall criteria with 37B triggered for each token. To accomplish effective inference and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free method for load balancing and sets a multi-token prediction training goal for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion diverse and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to completely harness its capabilities. Comprehensive assessments expose that DeepSeek-V3 outperforms other open-source designs and attains efficiency comparable to leading closed-source designs. Despite its exceptional efficiency, DeepSeek-V3 requires just 2.788 M H800 GPU hours for its full training. In addition, its training process is extremely 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 effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free method for load balancing, which decreases the efficiency destruction that emerges from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) goal and show it advantageous to design performance. It can also be used for speculative decoding for reasoning acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 combined accuracy training framework and, for the first time, validate the expediency and efficiency of FP8 training on a very large-scale design.
– Through co-design of algorithms, frameworks, and hardware, we overcome the communication traffic jam in cross-node MoE training, almost achieving complete computation-communication overlap.
This significantly improves our training efficiency and lowers the training expenses, enabling us to even more scale up the model size without extra overhead.
– At an economical expense of only 2.664 M H800 GPU hours, we finish the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently 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 ingenious methodology to distill reasoning abilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series designs, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly incorporates the confirmation and reflection patterns of R1 into DeepSeek-V3 and significantly enhances its thinking efficiency. Meanwhile, we likewise keep a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The total size of DeepSeek-V3 designs 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 performance and flexibility, we have actually partnered with open-source communities and hardware vendors to provide numerous ways to run the design locally. For step-by-step guidance, take a look at Section 6: How_to Run_Locally.

For designers wanting to dive deeper, we advise checking out README_WEIGHTS. md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is presently under active advancement within the community, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are shown in bold. Scores with a gap not going beyond 0.3 are thought about to be at the very same level. DeepSeek-V3 achieves the finest efficiency on many benchmarks, especially on math and code jobs. For more evaluation details, please examine our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models larger than 67B)

All models are assessed in a setup that restricts the output length to 8K. Benchmarks including less than 1000 samples are tested numerous times using differing temperature level settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise displays competitive efficiency versus frontier closed-source models.

Open Ended Generation Evaluation

English open-ended discussion assessments. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can talk with DeepSeek-V3 on DeepSeek’s main website: chat.deepseek.com

We also provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be deployed locally using the following hardware and open-source neighborhood software application:

DeepSeek-Infer Demo: We offer a basic and light-weight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables effective FP8 and BF16 inference for regional and cloud implementation.
TensorRT-LLM: Currently supports BF16 reasoning 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 offer FP8 weights. If you require BF16 weights for experimentation, you can utilize the offered 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 straight 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 inference folder and set up dependences noted in requirements.txt. Easiest method is to utilize a package manager like conda or uv to create a new virtual environment and set up 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 specific format:

Run

Then you can talk with DeepSeek-V3:

Or batch inference on an offered file:

6.2 Inference with SGLang (recommended)

SGLang presently optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing cutting edge latency and throughput efficiency amongst open-source structures.

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

SGLang also supports multi-node tensor parallelism, allowing you to run this design on several network-connected makers.

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

Here are the launch guidelines 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 reasoning and serving structure tailored for large language designs, now supports DeepSeek-V3. It uses both offline pipeline processing and online implementation capabilities, flawlessly integrating with PyTorch-based workflows.

For extensive detailed 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, offering accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in progress and will be launched quickly. You can access the customized branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the brand-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 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM offers pipeline parallelism permitting you to run this model on multiple devices connected by networks. For in-depth guidance, please refer to the vLLM instructions. Please do not hesitate to follow the improvement strategy too.

6.6 Recommended Inference Functionality with AMD GPUs

In collaboration with the AMD group, we have accomplished Day-One support for AMD GPUs utilizing SGLang, with full compatibility for both FP8 and BF16 precision. For in-depth assistance, please describe the SGLang instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend community has effectively adapted the BF16 version of DeepSeek-V3. For step-by-step assistance on Ascend NPUs, please follow the guidelines here.

7. License

This code repository is accredited under the MIT License. Using DeepSeek-V3 Base/Chat models undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial usage.