Overview

  • Founded Date May 16, 1925
  • Sectors سائقين
  • Posted Jobs 0
  • Viewed 6

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 achieve effective inference and economical training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free technique for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to completely harness its capabilities. Comprehensive assessments expose that DeepSeek-V3 outshines other open-source designs and achieves efficiency comparable to leading closed-source models. Despite its outstanding performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is remarkably steady. Throughout the whole 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 method for load balancing, which lessens the efficiency deterioration that develops from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) goal and prove it helpful to model efficiency. It can likewise be used for speculative decoding for reasoning acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 blended precision training structure and, for the very first time, verify the expediency and effectiveness of FP8 training on an extremely massive design.
– Through co-design of algorithms, structures, and hardware, we conquer the communication traffic jam in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This substantially boosts our training performance and decreases the training costs, enabling us to further scale up the design size without additional overhead.
– At a cost-effective cost of only 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently greatest open-source base model. The subsequent training phases after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an ingenious method to distill thinking abilities from the long-Chain-of-Thought (CoT) model, particularly from one of the DeepSeek R1 series models, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and especially enhances its reasoning efficiency. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3.

3. Model Downloads

The overall 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 ensure ideal efficiency and versatility, we have actually partnered with open-source communities and hardware vendors to supply numerous methods to run the model in your area. For step-by-step assistance, inspect out Section 6: How_to Run_Locally.

For designers seeking to dive deeper, we suggest 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 revealed in bold. Scores with a space not going beyond 0.3 are considered to be at the exact same level. DeepSeek-V3 achieves the best efficiency on most criteria, especially on mathematics and code jobs. For more evaluation details, please inspect our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are assessed in a configuration that restricts the output length to 8K. Benchmarks including fewer than 1000 samples are evaluated several times using differing temperature settings to obtain robust final outcomes. DeepSeek-V3 stands as the best-performing open-source design, and also shows competitive efficiency versus frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion evaluations. For AlpacaEval 2.0, we utilize 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 released locally using the following hardware and open-source neighborhood software application:

DeepSeek-Infer Demo: We supply an easy and lightweight demo for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud deployment.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support 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 gadgets.
Since FP8 training is natively embraced in our structure, we just provide FP8 weights. If you need BF16 weights for experimentation, you can utilize the provided conversion script to perform the improvement.

Here is an example of converting FP8 weights to BF16:

Hugging Face’s Transformers has actually 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 reasoning folder and set up reliances listed in requirements.txt. Easiest method is to use a plan supervisor like conda or uv to develop a brand-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 design weights to a particular format:

Run

Then you can chat with DeepSeek-V3:

Or batch inference on an offered file:

6.2 Inference with SGLang (recommended)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing advanced latency and throughput performance amongst open-source structures.

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

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

Multi-Token Prediction (MTP) is 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 (suggested)

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

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

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 model, offering precision alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in progress and will be launched soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 assistance through the following link to experience the new functions straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 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 offers pipeline parallelism permitting you to run this design on multiple makers linked by networks. For comprehensive assistance, please describe the vLLM guidelines. Please do not hesitate to follow the improvement strategy too.

6.6 Recommended Inference Functionality with AMD GPUs

In cooperation with the AMD group, we have actually attained Day-One assistance for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 accuracy. For detailed assistance, please describe the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend community has actually successfully adjusted the BF16 variation of DeepSeek-V3. For detailed assistance 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 goes through the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports industrial usage.