
Officialindustrialproducts
Add a review FollowOverview
-
Founded Date March 16, 1934
-
Sectors Telecommunications
-
Posted Jobs 0
-
Viewed 6
Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall criteria with 37B triggered for each token. To accomplish effective inference and affordable training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free method for load balancing and sets a multi-token prediction training objective for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its abilities. Comprehensive evaluations expose that DeepSeek-V3 outperforms other open-source models and attains efficiency comparable to leading closed-source models. Despite its outstanding performance, DeepSeek-V3 needs just 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is remarkably stable. Throughout the entire 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 method for load balancing, which reduces the efficiency destruction that develops from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it helpful to model efficiency. It can also be used for speculative decoding for reasoning acceleration.
Pre-Training: Towards Ultimate Training Efficiency
– We develop an FP8 combined accuracy training structure and, for the very first time, confirm the expediency and effectiveness of FP8 training on an incredibly large-scale model.
– Through co-design of algorithms, frameworks, and hardware, we conquer the communication bottleneck in cross-node MoE training, almost achieving complete computation-communication overlap.
This significantly improves our training performance and lowers the training costs, allowing us to even more scale up the model size without extra overhead.
– At an affordable cost of just 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently strongest open-source base design. 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 methodology to distill reasoning abilities from the long-Chain-of-Thought (CoT) model, particularly from among the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably enhances 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 models on Hugging Face is 685B, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To guarantee optimal performance and versatility, we have partnered with open-source communities and hardware suppliers to offer numerous methods to run the model locally. For step-by-step assistance, inspect out Section 6: How_to Run_Locally.
For designers aiming to dive much 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 development within the neighborhood, and we invite your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best outcomes are displayed in vibrant. Scores with a space not going beyond 0.3 are considered to be at the very same level. DeepSeek-V3 accomplishes the finest performance on a lot of criteria, particularly 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 throughout all context window lengths as much as 128K.
Chat Model
Standard Benchmarks (Models larger than 67B)
All designs are examined in a configuration that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are checked numerous times utilizing differing temperature level settings to obtain robust results. DeepSeek-V3 stands as the best-performing open-source model, and likewise displays competitive performance versus frontier closed-source models.
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 talk with DeepSeek-V3 on DeepSeek’s official website: chat.deepseek.com
We also supply OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com
6. How to Run Locally
DeepSeek-V3 can be released in your area using the following hardware and open-source community software application:
DeepSeek-Infer Demo: We provide a simple and light-weight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 inference for regional and cloud implementation.
TensorRT-LLM: Currently supports BF16 reasoning 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 design on AMD GPUs via 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 framework, we just offer FP8 weights. If you need BF16 weights for experimentation, you can use the supplied conversion script to perform the transformation.
Here is an example of transforming 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 dependences noted in requirements.txt. Easiest method is to use a bundle manager 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 specific format:
Run
Then you can chat with DeepSeek-V3:
Or batch reasoning on a given file:
6.2 Inference with SGLang (recommended)
SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering cutting edge latency and throughput performance among open-source structures.
Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust solution.
SGLang also supports multi-node tensor parallelism, enabling you to run this model on several network-connected devices.
Multi-Token Prediction (MTP) is in development, and development can be tracked in the optimization plan.
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 versatile and high-performance inference and serving structure customized for large language models, now supports DeepSeek-V3. It uses both offline pipeline processing and online release capabilities, flawlessly incorporating with PyTorch-based workflows.
For extensive detailed guidelines on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (recommended)
TensorRT-LLM now supports the DeepSeek-V3 model, using precision alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in progress and will be released soon. You can access the custom-made 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 (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 uses pipeline parallelism enabling you to run this model on numerous makers connected by networks. For comprehensive guidance, please refer to the vLLM instructions. Please feel free to follow the enhancement plan also.
6.6 Recommended Inference Functionality with AMD GPUs
In partnership with the AMD group, we have assistance for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For in-depth guidance, please refer to the SGLang guidelines.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE structure from the Huawei Ascend neighborhood has effectively 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 certified under the MIT License. Using DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports business use.