Walkandtalkrentals

GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To accomplish effective reasoning and cost-effective training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely validated in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free technique for load balancing and sets a multi-token prediction training goal for stronger efficiency. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive examinations reveal that DeepSeek-V3 outshines other open-source designs and attains performance equivalent to leading closed-source designs. Despite its outstanding efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is incredibly steady. Throughout the entire training process, we did not experience any irrecoverable loss spikes or carry out 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 minimizes the efficiency degradation that develops from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it useful to design efficiency. It can also be used for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 combined accuracy training framework and, for the very first time, confirm the feasibility and effectiveness of FP8 training on a very massive design.
– Through co-design of algorithms, structures, and hardware, we conquer the communication traffic jam in cross-node MoE training, almost attaining complete computation-communication overlap.
This substantially enhances our training effectiveness and reduces the training costs, allowing us to further scale up the design size without additional overhead.
– At an affordable expense 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 model. The subsequent training stages after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We present an ingenious methodology to distill reasoning abilities from the long-Chain-of-Thought (CoT) model, particularly 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 especially enhances its thinking efficiency. Meanwhile, we also maintain 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 guarantee optimal efficiency and flexibility, we have partnered with open-source neighborhoods and hardware suppliers to offer multiple methods to run the model locally. For step-by-step guidance, take a look at Section 6: How_to Run_Locally.

For designers seeking to dive deeper, we suggest 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 community, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are displayed in strong. Scores with a gap not going beyond 0.3 are considered to be at the exact same level. DeepSeek-V3 achieves the best performance on many standards, specifically on math and code jobs. For more examination details, please inspect 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 models are assessed in a setup that limits the output length to 8K. Benchmarks containing less than 1000 samples are tested several times using varying temperature settings to derive robust last results. 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 examinations. 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 also provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be released locally using the following hardware and open-source community software:

DeepSeek-Infer Demo: We offer a basic and light-weight demo for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 assistance coming soon.
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 through SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively embraced in our structure, we only offer FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to carry out 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 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 install dependencies noted in requirements.txt. Easiest method is to utilize a package supervisor like conda or uv to produce a brand-new virtual environment and set up the reliances.

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 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 among open-source structures.

Notably, SGLang v0.4.1 completely supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly flexible and robust option.

SGLang also supports multi-node tensor parallelism, enabling you to run this design on several network-connected makers.

Multi-Token Prediction (MTP) is in advancement, and progress can be tracked in the optimization plan.

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 (recommended)

LMDeploy, a flexible and high-performance reasoning and serving structure customized for large designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online release abilities, flawlessly incorporating with PyTorch-based workflows.

For thorough detailed guidelines on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 design, using accuracy choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in development and will be launched quickly. You can access the custom-made branch of TRTLLM particularly 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 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM uses pipeline parallelism permitting you to run this design on several makers connected by networks. For in-depth guidance, please refer to the vLLM directions. Please do not hesitate to follow the improvement strategy as well.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have actually accomplished Day-One assistance for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has actually successfully adapted the BF16 variation of DeepSeek-V3. For detailed guidance 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 designs is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports business use.