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  • Founded Date 7 9 月, 1993
  • Sectors 生產/設備專員
  • Posted Jobs 0
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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 overall specifications with 37B activated for each token. To accomplish efficient inference and cost-efficient training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token forecast training objective for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion diverse and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its capabilities. Comprehensive assessments expose that DeepSeek-V3 outperforms other open-source models and achieves efficiency comparable to leading closed-source designs. Despite its outstanding efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is extremely stable. Throughout the whole training process, 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 technique for load balancing, which minimizes the efficiency degradation that occurs from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) goal and prove it useful to model performance. It can also be utilized for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 combined precision training framework and, for the first time, validate the expediency and efficiency of FP8 training on a very massive design.
– Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly attaining complete computation-communication overlap.
This significantly improves our training effectiveness and minimizes the training expenses, allowing us to even more scale up the design size without additional overhead.
– At an economical cost 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 model. The subsequent training phases after pre-training need only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We present an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) design, particularly from among the DeepSeek R1 series designs, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and especially enhances its thinking performance. Meanwhile, we also keep a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The total 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 make sure optimum performance and versatility, we have actually partnered with open-source neighborhoods and hardware suppliers to supply numerous methods to run the design locally. For detailed assistance, have a look at Section 6: How_to Run_Locally.

For designers wanting to dive deeper, we recommend exploring README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is presently under active development within the neighborhood, 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 surpassing 0.3 are thought about to be at the exact same level. DeepSeek-V3 attains the very best efficiency on most criteria, particularly on mathematics and code tasks. For more examination details, please inspect our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are assessed in a configuration that limits the output length to 8K. Benchmarks including fewer than 1000 samples are checked multiple times utilizing differing temperature settings to derive robust results. DeepSeek-V3 stands as the best-performing open-source model, and also displays competitive performance versus frontier closed-source designs.

Open Ended Generation Evaluation

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

DeepSeek-Infer Demo: We provide an easy and light-weight demonstration for FP8 and BF16 inference.
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 reasoning and INT4/8 quantization, with FP8 assistance coming quickly.
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 through SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively adopted in our structure, we just offer FP8 weights. If you require BF16 weights for experimentation, you can use 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 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 set up dependences noted in requirements.txt. Easiest method is to utilize a package manager like conda or uv to produce a new virtual environment and set up the reliances.

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 inference on a given file:

6.2 Inference with SGLang (suggested)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering modern latency and throughput performance among open-source frameworks.

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

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

Multi-Token Prediction (MTP) remains in advancement, 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 framework tailored for big language designs, now supports DeepSeek-V3. It uses both offline pipeline processing and online implementation capabilities, flawlessly incorporating with PyTorch-based workflows.

For comprehensive detailed directions on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (advised)

TensorRT-LLM now supports the DeepSeek-V3 design, providing accuracy alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be launched soon. You can access the of TRTLLM specifically for DeepSeek-V3 support 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 techniques, vLLM uses pipeline parallelism allowing you to run this model on several machines linked by networks. For in-depth assistance, please describe the vLLM guidelines. Please do not hesitate to follow the improvement plan too.

6.6 Recommended Inference Functionality with AMD GPUs

In collaboration with the AMD team, we have actually accomplished Day-One assistance for AMD GPUs utilizing SGLang, with full compatibility for both FP8 and BF16 precision. For in-depth guidance, please refer to the SGLang instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has actually successfully adapted the BF16 variation of DeepSeek-V3. For detailed 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 designs undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.

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