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Founded Date 17 12 月, 2011
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Sectors 建築/景觀設計師
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Viewed 4
Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B total criteria with 37B triggered for each token. To attain effective inference and economical 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 goal for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its abilities. Comprehensive evaluations reveal that DeepSeek-V3 surpasses other open-source designs and accomplishes performance equivalent to leading closed-source designs. Despite its exceptional performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its complete training. In addition, its training process is extremely 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 efficient architecture of DeepSeek-V2, we leader an auxiliary-loss-free technique for load balancing, which decreases the efficiency deterioration that develops from encouraging load balancing.
– We examine a Multi-Token Prediction (MTP) objective and show it beneficial to design efficiency. It can likewise be used for speculative decoding for reasoning velocity.
Pre-Training: Towards Ultimate Training Efficiency
– We create an FP8 blended precision training framework and, for the first time, verify the expediency and effectiveness of FP8 training on an extremely large-scale model.
– Through co-design of algorithms, structures, and hardware, we overcome the communication bottleneck in cross-node MoE training, almost achieving complete computation-communication overlap.
This substantially enhances our training performance and minimizes the training costs, enabling us to even more scale up the design size without additional overhead.
– At an economical cost of just 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 thinking capabilities from the long-Chain-of-Thought (CoT) model, particularly from among the DeepSeek R1 series models, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly includes the verification and reflection patterns of R1 into DeepSeek-V3 and significantly enhances its reasoning performance. Meanwhile, we likewise preserve a control over the output style 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 make sure optimum performance and flexibility, we have actually partnered with open-source communities and hardware suppliers to provide numerous ways to run the design in your area. For step-by-step assistance, examine out Section 6: How_to Run_Locally.
For developers aiming 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 advancement within the neighborhood, and we invite your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best outcomes are displayed in bold. Scores with a gap not exceeding 0.3 are considered to be at the exact same level. DeepSeek-V3 attains the very best efficiency on a lot of criteria, especially on math and code jobs. For more examination details, please examine our paper.
Context Window
Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well throughout all context window lengths approximately 128K.
Chat Model
Standard Benchmarks (Models larger than 67B)
All models are assessed in a configuration that restricts the output length to 8K. Benchmarks consisting of less than 1000 samples are tested numerous times utilizing differing temperature settings to obtain robust last outcomes. DeepSeek-V3 stands as the best-performing open-source model, and also shows competitive performance versus 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 talk with DeepSeek-V3 on DeepSeek’s main website: chat.deepseek.com
We likewise offer 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 simple and light-weight 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 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 design with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 design on AMD GPUs by means of 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 just offer FP8 weights. If you require BF16 weights for experimentation, you can utilize the supplied conversion script to perform the improvement.
Here is an example of converting FP8 weights to BF16:
Hugging Face’s Transformers has actually not been straight supported yet. **
6.1 Inference with (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 inference folder and install reliances listed in requirements.txt. Easiest way is to utilize a plan manager like conda or uv to produce a new virtual environment and install the dependences.
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 chat with DeepSeek-V3:
Or batch inference on an offered file:
6.2 Inference with SGLang (suggested)
SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing modern latency and throughput efficiency among open-source frameworks.
Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely flexible and robust solution.
SGLang likewise supports multi-node tensor parallelism, allowing you to run this model on multiple network-connected devices.
Multi-Token Prediction (MTP) is in advancement, and progress can be tracked in the optimization strategy.
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 (advised)
LMDeploy, a flexible and high-performance reasoning and serving structure customized for big language designs, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation capabilities, flawlessly incorporating with PyTorch-based workflows.
For comprehensive step-by-step 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 model, providing precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be released quickly. You can access the custom branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the 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 standard methods, vLLM uses pipeline parallelism enabling you to run this model on multiple machines linked by networks. For comprehensive assistance, please describe the vLLM guidelines. Please do not hesitate to follow the enhancement plan as well.
6.6 Recommended Inference Functionality with AMD GPUs
In partnership with the AMD group, we have actually achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. 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 adjusted the BF16 version of DeepSeek-V3. For step-by-step assistance on Ascend NPUs, please follow the instructions here.
7. License
This code repository is licensed under the MIT License. Making use of DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports industrial use.