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GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B total parameters with 37B triggered for each token. To achieve effective reasoning and affordable training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly verified in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free technique 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 surpasses other open-source designs and achieves efficiency similar to leading closed-source designs. Despite its outstanding performance, DeepSeek-V3 requires just 2.788 M H800 GPU hours for its complete training. In addition, its training process is extremely stable. Throughout the whole 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 efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free method for load balancing, which reduces the performance destruction that arises from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) goal and show it beneficial to model performance. It can likewise be utilized for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 combined accuracy training framework and, for the first time, validate the expediency and effectiveness of FP8 training on an extremely massive 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 substantially improves our training efficiency and lowers the training expenses, 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 greatest 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 approach to boil down reasoning capabilities from the long-Chain-of-Thought (CoT) model, particularly from among the DeepSeek R1 series designs, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the confirmation and reflection patterns of R1 into DeepSeek-V3 and notably enhances its reasoning efficiency. Meanwhile, we also maintain a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The overall 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 ensure optimum efficiency and versatility, we have partnered with open-source neighborhoods and hardware suppliers to offer numerous methods to run the model in your area. For detailed guidance, check out Section 6: How_to Run_Locally.

For designers wanting to dive deeper, we recommend checking out README_WEIGHTS. md for information 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 shown in bold. Scores with a gap not going beyond 0.3 are thought about to be at the exact same level. DeepSeek-V3 attains the very best efficiency on most standards, especially on mathematics 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 performs well throughout all context window lengths as much as 128K.

Chat Model

Standard Benchmarks (Models larger than 67B)

All designs are evaluated in a configuration that restricts the output length to 8K. Benchmarks including less than 1000 samples are evaluated numerous times using varying temperature settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source model, and likewise shows competitive performance against frontier closed-source models.

Open Ended Generation Evaluation

English open-ended discussion examinations. 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 likewise supply OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be released in your area utilizing the following hardware and open-source community software:

DeepSeek-Infer Demo: We provide a basic and light-weight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 inference for local and .
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support 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 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 adopted in our framework, we just provide FP8 weights. If you need BF16 weights for experimentation, you can use the provided conversion script to perform the transformation.

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 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 inference folder and set up reliances noted in requirements.txt. Easiest method is to utilize a bundle manager like conda or uv to produce 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 design weights to a specific format:

Run

Then you can chat with DeepSeek-V3:

Or batch reasoning on a provided file:

6.2 Inference with SGLang (advised)

SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing modern latency and throughput performance amongst 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 design on several network-connected makers.

Multi-Token Prediction (MTP) remains in development, and progress can be tracked in the optimization strategy.

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 reasoning and serving framework customized for big language designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online deployment abilities, perfectly incorporating with PyTorch-based workflows.

For extensive step-by-step directions on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (suggested)

TensorRT-LLM now supports the DeepSeek-V3 model, using accuracy alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in development and will be released quickly. You can access the custom-made branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the new functions 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 techniques, vLLM uses pipeline parallelism enabling you to run this model on numerous devices linked by networks. For comprehensive guidance, please refer to the vLLM directions. Please feel free to follow the improvement plan also.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD group, we have actually achieved Day-One assistance for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For detailed guidance, please refer to the SGLang directions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend neighborhood has effectively adapted the BF16 variation of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the instructions here.

7. License

This code repository is certified under the MIT License. The use of DeepSeek-V3 Base/Chat models undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.