
C 1 Support
Add a review FollowOverview
-
Founded Date August 10, 1983
-
Sectors Security Guard
-
Posted Jobs 0
-
Viewed 7
Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total specifications with 37B activated for each token. To attain effective inference and cost-effective training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely verified in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers 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 diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to fully harness its abilities. Comprehensive evaluations expose that DeepSeek-V3 outperforms other open-source designs and achieves performance equivalent to leading closed-source designs. Despite its outstanding performance, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training process is remarkably stable. Throughout the entire training process, 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 degradation that occurs from motivating load balancing.
– We investigate a Multi-Token Prediction (MTP) goal and show it beneficial to . It can likewise be used for speculative decoding for reasoning acceleration.
Pre-Training: Towards Ultimate Training Efficiency
– We create an FP8 blended accuracy training framework and, for the very 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 conquer the interaction traffic jam in cross-node MoE training, almost attaining full computation-communication overlap.
This significantly enhances our training effectiveness and lowers the training expenses, enabling us to further scale up the model size without extra overhead.
– At an affordable cost of only 2.664 M H800 GPU hours, we finish the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently strongest open-source base model. The subsequent training phases after pre-training require only 0.1 M GPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
– We introduce an innovative method to distill thinking abilities from the long-Chain-of-Thought (CoT) design, specifically from one of the DeepSeek R1 series models, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly includes the verification and reflection patterns of R1 into DeepSeek-V3 and significantly improves its reasoning performance. Meanwhile, we also keep 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, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To ensure ideal efficiency and versatility, we have partnered with open-source neighborhoods and hardware suppliers to provide multiple ways to run the design locally. For step-by-step assistance, have a look at Section 6: How_to Run_Locally.
For developers aiming to dive much 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 advancement within the neighborhood, and we welcome your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best outcomes are displayed in bold. Scores with a gap not surpassing 0.3 are considered to be at the very same level. DeepSeek-V3 accomplishes the very best efficiency on most benchmarks, specifically on math and code jobs. For more assessment details, please check 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 bigger than 67B)
All models are assessed in a setup that limits the output length to 8K. Benchmarks consisting of fewer than 1000 samples are evaluated multiple times utilizing differing temperature level settings to obtain robust results. DeepSeek-V3 stands as the best-performing open-source model, and likewise shows competitive efficiency versus frontier closed-source models.
Open Ended Generation Evaluation
English open-ended conversation evaluations. 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 official website: chat.deepseek.com
We likewise 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:
DeepSeek-Infer Demo: We provide a simple and light-weight demonstration for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables efficient FP8 and BF16 reasoning for regional and cloud release.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance 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 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 supply FP8 weights. If you require BF16 weights for experimentation, you can utilize the supplied conversion script to carry out the improvement.
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 dependencies noted in requirements.txt. Easiest method is to use a plan supervisor like conda or uv to create a brand-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 design weights to a specific format:
Run
Then you can talk with DeepSeek-V3:
Or batch reasoning 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 state-of-the-art latency and throughput efficiency amongst open-source structures.
Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely flexible and robust service.
SGLang also supports multi-node tensor parallelism, enabling you to run this design on several network-connected devices.
Multi-Token Prediction (MTP) remains in advancement, and development can be tracked in the optimization strategy.
Here are the launch instructions from the SGLang group: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
6.3 Inference with LMDeploy (suggested)
LMDeploy, a versatile and high-performance reasoning and serving structure tailored for big language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation capabilities, seamlessly integrating with PyTorch-based workflows.
For thorough 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 model, using precision choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be released soon. You can access the customized branch 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 (recommended)
vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic techniques, vLLM provides pipeline parallelism allowing you to run this design on several makers connected by networks. For detailed assistance, please describe the vLLM directions. Please feel free to follow the improvement strategy as well.
6.6 Recommended Inference Functionality with AMD GPUs
In cooperation with the AMD group, we have actually attained Day-One support for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 accuracy. For detailed assistance, please describe the SGLang directions.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE framework from the Huawei Ascend community has effectively adjusted the BF16 variation of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the guidelines here.
7. License
This code repository is licensed under the MIT License. Using DeepSeek-V3 Base/Chat designs undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports business use.