
Emploi Securite
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Fecha de fundación octubre 9, 1925
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Sectores Educación
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Retos publicados 0
Sobre la Entidad
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B overall criteria with 37B activated for each token. To achieve efficient inference and cost-efficient 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 method for load balancing and sets a multi-token forecast training objective for stronger efficiency. We pre-train DeepSeek-V3 on 14.8 trillion diverse and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to completely harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 exceeds other open-source models and achieves efficiency equivalent to leading closed-source . Despite its excellent performance, DeepSeek-V3 needs just 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is incredibly stable. Throughout the whole 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 leader an auxiliary-loss-free strategy 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 model efficiency. It can also be utilized for speculative decoding for reasoning acceleration.
Pre-Training: Towards Ultimate Training Efficiency
– We develop an FP8 mixed accuracy training structure and, for the very first time, confirm the expediency and effectiveness of FP8 training on a very massive design. – Through co-design of algorithms, structures, and hardware, we overcome the communication traffic jam in cross-node MoE training, nearly attaining complete computation-communication overlap. This significantly enhances our training efficiency and lowers the training expenses, allowing us to even more scale up the model size without extra overhead. – At an economical cost of only 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently 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 present an ingenious method to boil down 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 integrates the verification and reflection patterns of R1 into DeepSeek-V3 and notably enhances its thinking performance. 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, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To make sure optimum efficiency and versatility, we have partnered with open-source neighborhoods and hardware suppliers to provide multiple methods to run the design locally. For detailed guidance, have a look at Section 6: How_to Run_Locally.
For designers looking to dive much 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 support is currently 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 strong. Scores with a space not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the finest efficiency on most criteria, especially on math and code jobs. For more assessment details, please inspect 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 evaluated in a setup that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are evaluated multiple times using differing temperature settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and also exhibits competitive performance against frontier closed-source designs.
Open Ended Generation Evaluation
English open-ended conversation evaluations. 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 main 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 in your area utilizing the following hardware and open-source community software:

DeepSeek-Infer Demo: We provide an easy and light-weight demonstration for FP8 and BF16 reasoning. SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly. LMDeploy: Enables effective FP8 and BF16 reasoning for regional and cloud deployment. TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance 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 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 structure, we only supply 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 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 reasoning folder and set up dependences listed in requirements.txt. Easiest way is to use a bundle supervisor like conda or uv to create a new virtual environment and set up 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 an offered file:
6.2 Inference with SGLang (suggested)
SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering advanced latency and throughput efficiency among open-source structures.
Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely versatile and robust option.
SGLang also supports multi-node tensor parallelism, allowing you to run this model on several network-connected devices.
Multi-Token Prediction (MTP) is in development, 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 flexible and high-performance inference and serving framework customized for big language designs, now supports DeepSeek-V3. It offers both offline pipeline processing and online release capabilities, perfectly incorporating 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 model, using precision choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be launched soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support 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 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM provides pipeline parallelism enabling you to run this model on several makers connected by networks. For in-depth guidance, please describe the vLLM directions. Please feel free to follow the enhancement plan too.

6.6 Recommended Inference Functionality with AMD GPUs
In collaboration with the AMD group, we have actually accomplished Day-One assistance for AMD GPUs using SGLang, with complete compatibility for both FP8 and BF16 precision. For in-depth guidance, please refer to the SGLang directions.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE structure from the Huawei Ascend community has effectively adjusted the BF16 variation of DeepSeek-V3. For detailed 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 goes through the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports commercial usage.