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@ -37,14 +37,15 @@
<a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-MODEL" style="margin: 2px;"> <a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-MODEL" style="margin: 2px;">
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a> </a>
<a href="https://github.com/myakoobi" style="margin: 2px;">
<img alt="Model License" src="https://avatars.githubusercontent.com/u/170466781?v=4" style="display: inline-block; vertical-align: middle;"/>
</a>
</div> </div>
<p align="center"> <p align="center">
<a href="DeepSeek_V3.pdf"><b>Paper Link</b>👁️</a> <a href="DeepSeek_V3.pdf"><b>Paper Link</b>👁️</a>
</p> </p>
## 1. Introduction ## 1. Introduction
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
@ -55,6 +56,7 @@ Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source
Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
In addition, its training process is remarkably stable. 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. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.
<p align="center"> <p align="center">
<img width="80%" src="figures/benchmark.png"> <img width="80%" src="figures/benchmark.png">
</p> </p>
@ -66,8 +68,8 @@ Throughout the entire training process, we did not experience any irrecoverable
**Architecture: Innovative Load Balancing Strategy and Training Objective** **Architecture: Innovative Load Balancing Strategy and Training Objective**
- On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing. - On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing.
- We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. - We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance.
It can also be used for speculative decoding for inference acceleration. It can also be used for speculative decoding for inference acceleration.
--- ---
@ -82,73 +84,73 @@ Throughout the entire training process, we did not experience any irrecoverable
**Post-Training: Knowledge Distillation from DeepSeek-R1** **Post-Training: Knowledge Distillation from DeepSeek-R1**
- We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of 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 improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3. - We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of 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 improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3.
--- ---
## 3. Model Downloads ## 3. Model Downloads
<div align="center"> <div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | | **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: | :------------: | :------------: | | :--------------: | :---------------: | :-------------------: | :----------------: | :--------------------------------------------------------------------: |
| DeepSeek-V3-Base | 671B | 37B | 128K | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) | | DeepSeek-V3-Base | 671B | 37B | 128K | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) |
| DeepSeek-V3 | 671B | 37B | 128K | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3) | | DeepSeek-V3 | 671B | 37B | 128K | [🤗 Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3) |
</div> </div>
> [!NOTE] > [!NOTE]
> The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.** > The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.\*\*
To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally). To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally).
For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md](./README_WEIGHTS.md) for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback. For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md](./README_WEIGHTS.md) for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback.
## 4. Evaluation Results ## 4. Evaluation Results
### Base Model ### Base Model
#### Standard Benchmarks #### Standard Benchmarks
<div align="center"> <div align="center">
| | Benchmark (Metric) | # Shots | DeepSeek-V2 | Qwen2.5 72B | LLaMA3.1 405B | DeepSeek-V3 |
| | Benchmark (Metric) | # Shots | DeepSeek-V2 | Qwen2.5 72B | LLaMA3.1 405B | DeepSeek-V3 | | ------------ | --------------------------- | ------- | ----------- | ----------- | ------------- | ----------- |
|---|-------------------|----------|--------|-------------|---------------|---------| | | Architecture | - | MoE | Dense | Dense | MoE |
| | Architecture | - | MoE | Dense | Dense | MoE | | | # Activated Params | - | 21B | 72B | 405B | 37B |
| | # Activated Params | - | 21B | 72B | 405B | 37B | | | # Total Params | - | 236B | 72B | 405B | 671B |
| | # Total Params | - | 236B | 72B | 405B | 671B | | English | Pile-test (BPB) | - | 0.606 | 0.638 | **0.542** | 0.548 |
| English | Pile-test (BPB) | - | 0.606 | 0.638 | **0.542** | 0.548 | | | BBH (EM) | 3-shot | 78.8 | 79.8 | 82.9 | **87.5** |
| | BBH (EM) | 3-shot | 78.8 | 79.8 | 82.9 | **87.5** | | | MMLU (Acc.) | 5-shot | 78.4 | 85.0 | 84.4 | **87.1** |
| | MMLU (Acc.) | 5-shot | 78.4 | 85.0 | 84.4 | **87.1** | | | MMLU-Redux (Acc.) | 5-shot | 75.6 | 83.2 | 81.3 | **86.2** |
| | MMLU-Redux (Acc.) | 5-shot | 75.6 | 83.2 | 81.3 | **86.2** | | | MMLU-Pro (Acc.) | 5-shot | 51.4 | 58.3 | 52.8 | **64.4** |
| | MMLU-Pro (Acc.) | 5-shot | 51.4 | 58.3 | 52.8 | **64.4** | | | DROP (F1) | 3-shot | 80.4 | 80.6 | 86.0 | **89.0** |
| | DROP (F1) | 3-shot | 80.4 | 80.6 | 86.0 | **89.0** | | | ARC-Easy (Acc.) | 25-shot | 97.6 | 98.4 | 98.4 | **98.9** |
| | ARC-Easy (Acc.) | 25-shot | 97.6 | 98.4 | 98.4 | **98.9** | | | ARC-Challenge (Acc.) | 25-shot | 92.2 | 94.5 | **95.3** | **95.3** |
| | ARC-Challenge (Acc.) | 25-shot | 92.2 | 94.5 | **95.3** | **95.3** | | | HellaSwag (Acc.) | 10-shot | 87.1 | 84.8 | **89.2** | 88.9 |
| | HellaSwag (Acc.) | 10-shot | 87.1 | 84.8 | **89.2** | 88.9 | | | PIQA (Acc.) | 0-shot | 83.9 | 82.6 | **85.9** | 84.7 |
| | PIQA (Acc.) | 0-shot | 83.9 | 82.6 | **85.9** | 84.7 | | | WinoGrande (Acc.) | 5-shot | **86.3** | 82.3 | 85.2 | 84.9 |
| | WinoGrande (Acc.) | 5-shot | **86.3** | 82.3 | 85.2 | 84.9 | | | RACE-Middle (Acc.) | 5-shot | 73.1 | 68.1 | **74.2** | 67.1 |
| | RACE-Middle (Acc.) | 5-shot | 73.1 | 68.1 | **74.2** | 67.1 | | | RACE-High (Acc.) | 5-shot | 52.6 | 50.3 | **56.8** | 51.3 |
| | RACE-High (Acc.) | 5-shot | 52.6 | 50.3 | **56.8** | 51.3 | | | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | **82.7** | **82.9** |
| | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | **82.7** | **82.9** | | | NaturalQuestions (EM) | 5-shot | 38.6 | 33.2 | **41.5** | 40.0 |
| | NaturalQuestions (EM) | 5-shot | 38.6 | 33.2 | **41.5** | 40.0 | | | AGIEval (Acc.) | 0-shot | 57.5 | 75.8 | 60.6 | **79.6** |
| | AGIEval (Acc.) | 0-shot | 57.5 | 75.8 | 60.6 | **79.6** | | Code | HumanEval (Pass@1) | 0-shot | 43.3 | 53.0 | 54.9 | **65.2** |
| Code | HumanEval (Pass@1) | 0-shot | 43.3 | 53.0 | 54.9 | **65.2** | | | MBPP (Pass@1) | 3-shot | 65.0 | 72.6 | 68.4 | **75.4** |
| | MBPP (Pass@1) | 3-shot | 65.0 | 72.6 | 68.4 | **75.4** | | | LiveCodeBench-Base (Pass@1) | 3-shot | 11.6 | 12.9 | 15.5 | **19.4** |
| | LiveCodeBench-Base (Pass@1) | 3-shot | 11.6 | 12.9 | 15.5 | **19.4** | | | CRUXEval-I (Acc.) | 2-shot | 52.5 | 59.1 | 58.5 | **67.3** |
| | CRUXEval-I (Acc.) | 2-shot | 52.5 | 59.1 | 58.5 | **67.3** | | | CRUXEval-O (Acc.) | 2-shot | 49.8 | 59.9 | 59.9 | **69.8** |
| | CRUXEval-O (Acc.) | 2-shot | 49.8 | 59.9 | 59.9 | **69.8** | | Math | GSM8K (EM) | 8-shot | 81.6 | 88.3 | 83.5 | **89.3** |
| Math | GSM8K (EM) | 8-shot | 81.6 | 88.3 | 83.5 | **89.3** | | | MATH (EM) | 4-shot | 43.4 | 54.4 | 49.0 | **61.6** |
| | MATH (EM) | 4-shot | 43.4 | 54.4 | 49.0 | **61.6** | | | MGSM (EM) | 8-shot | 63.6 | 76.2 | 69.9 | **79.8** |
| | MGSM (EM) | 8-shot | 63.6 | 76.2 | 69.9 | **79.8** | | | CMath (EM) | 3-shot | 78.7 | 84.5 | 77.3 | **90.7** |
| | CMath (EM) | 3-shot | 78.7 | 84.5 | 77.3 | **90.7** | | Chinese | CLUEWSC (EM) | 5-shot | 82.0 | 82.5 | **83.0** | 82.7 |
| Chinese | CLUEWSC (EM) | 5-shot | 82.0 | 82.5 | **83.0** | 82.7 | | | C-Eval (Acc.) | 5-shot | 81.4 | 89.2 | 72.5 | **90.1** |
| | C-Eval (Acc.) | 5-shot | 81.4 | 89.2 | 72.5 | **90.1** | | | CMMLU (Acc.) | 5-shot | 84.0 | **89.5** | 73.7 | 88.8 |
| | CMMLU (Acc.) | 5-shot | 84.0 | **89.5** | 73.7 | 88.8 | | | CMRC (EM) | 1-shot | **77.4** | 75.8 | 76.0 | 76.3 |
| | CMRC (EM) | 1-shot | **77.4** | 75.8 | 76.0 | 76.3 | | | C3 (Acc.) | 0-shot | 77.4 | 76.7 | **79.7** | 78.6 |
| | C3 (Acc.) | 0-shot | 77.4 | 76.7 | **79.7** | 78.6 | | | CCPM (Acc.) | 0-shot | **93.0** | 88.5 | 78.6 | 92.0 |
| | CCPM (Acc.) | 0-shot | **93.0** | 88.5 | 78.6 | 92.0 | | Multilingual | MMMLU-non-English (Acc.) | 5-shot | 64.0 | 74.8 | 73.8 | **79.4** |
| Multilingual | MMMLU-non-English (Acc.) | 5-shot | 64.0 | 74.8 | 73.8 | **79.4** |
</div> </div>
@ -157,72 +159,72 @@ For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md
> For more evaluation details, please check our paper. > For more evaluation details, please check our paper.
#### Context Window #### Context Window
<p align="center"> <p align="center">
<img width="80%" src="figures/niah.png"> <img width="80%" src="figures/niah.png">
</p> </p>
Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to **128K**. Evaluation results on the `Needle In A Haystack` (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to **128K**.
### Chat Model ### Chat Model
#### Standard Benchmarks (Models larger than 67B) #### Standard Benchmarks (Models larger than 67B)
<div align="center"> <div align="center">
| | **Benchmark (Metric)** | **DeepSeek V2-0506** | **DeepSeek V2.5-0905** | **Qwen2.5 72B-Inst.** | **Llama3.1 405B-Inst.** | **Claude-3.5-Sonnet-1022** | **GPT-4o 0513** | **DeepSeek V3** | | | **Benchmark (Metric)** | **DeepSeek V2-0506** | **DeepSeek V2.5-0905** | **Qwen2.5 72B-Inst.** | **Llama3.1 405B-Inst.** | **Claude-3.5-Sonnet-1022** | **GPT-4o 0513** | **DeepSeek V3** |
|---|---------------------|---------------------|----------------------|---------------------|----------------------|---------------------------|----------------|----------------| | ------- | -------------------------- | -------------------- | ---------------------- | --------------------- | ----------------------- | -------------------------- | --------------- | --------------- |
| | Architecture | MoE | MoE | Dense | Dense | - | - | MoE | | | Architecture | MoE | MoE | Dense | Dense | - | - | MoE |
| | # Activated Params | 21B | 21B | 72B | 405B | - | - | 37B | | | # Activated Params | 21B | 21B | 72B | 405B | - | - | 37B |
| | # Total Params | 236B | 236B | 72B | 405B | - | - | 671B | | | # Total Params | 236B | 236B | 72B | 405B | - | - | 671B |
| English | MMLU (EM) | 78.2 | 80.6 | 85.3 | **88.6** | **88.3** | 87.2 | **88.5** | | English | MMLU (EM) | 78.2 | 80.6 | 85.3 | **88.6** | **88.3** | 87.2 | **88.5** |
| | MMLU-Redux (EM) | 77.9 | 80.3 | 85.6 | 86.2 | **88.9** | 88.0 | **89.1** | | | MMLU-Redux (EM) | 77.9 | 80.3 | 85.6 | 86.2 | **88.9** | 88.0 | **89.1** |
| | MMLU-Pro (EM) | 58.5 | 66.2 | 71.6 | 73.3 | **78.0** | 72.6 | 75.9 | | | MMLU-Pro (EM) | 58.5 | 66.2 | 71.6 | 73.3 | **78.0** | 72.6 | 75.9 |
| | DROP (3-shot F1) | 83.0 | 87.8 | 76.7 | 88.7 | 88.3 | 83.7 | **91.6** | | | DROP (3-shot F1) | 83.0 | 87.8 | 76.7 | 88.7 | 88.3 | 83.7 | **91.6** |
| | IF-Eval (Prompt Strict) | 57.7 | 80.6 | 84.1 | 86.0 | **86.5** | 84.3 | 86.1 | | | IF-Eval (Prompt Strict) | 57.7 | 80.6 | 84.1 | 86.0 | **86.5** | 84.3 | 86.1 |
| | GPQA-Diamond (Pass@1) | 35.3 | 41.3 | 49.0 | 51.1 | **65.0** | 49.9 | 59.1 | | | GPQA-Diamond (Pass@1) | 35.3 | 41.3 | 49.0 | 51.1 | **65.0** | 49.9 | 59.1 |
| | SimpleQA (Correct) | 9.0 | 10.2 | 9.1 | 17.1 | 28.4 | **38.2** | 24.9 | | | SimpleQA (Correct) | 9.0 | 10.2 | 9.1 | 17.1 | 28.4 | **38.2** | 24.9 |
| | FRAMES (Acc.) | 66.9 | 65.4 | 69.8 | 70.0 | 72.5 | **80.5** | 73.3 | | | FRAMES (Acc.) | 66.9 | 65.4 | 69.8 | 70.0 | 72.5 | **80.5** | 73.3 |
| | LongBench v2 (Acc.) | 31.6 | 35.4 | 39.4 | 36.1 | 41.0 | 48.1 | **48.7** | | | LongBench v2 (Acc.) | 31.6 | 35.4 | 39.4 | 36.1 | 41.0 | 48.1 | **48.7** |
| Code | HumanEval-Mul (Pass@1) | 69.3 | 77.4 | 77.3 | 77.2 | 81.7 | 80.5 | **82.6** | | Code | HumanEval-Mul (Pass@1) | 69.3 | 77.4 | 77.3 | 77.2 | 81.7 | 80.5 | **82.6** |
| | LiveCodeBench (Pass@1-COT) | 18.8 | 29.2 | 31.1 | 28.4 | 36.3 | 33.4 | **40.5** | | | LiveCodeBench (Pass@1-COT) | 18.8 | 29.2 | 31.1 | 28.4 | 36.3 | 33.4 | **40.5** |
| | LiveCodeBench (Pass@1) | 20.3 | 28.4 | 28.7 | 30.1 | 32.8 | 34.2 | **37.6** | | | LiveCodeBench (Pass@1) | 20.3 | 28.4 | 28.7 | 30.1 | 32.8 | 34.2 | **37.6** |
| | Codeforces (Percentile) | 17.5 | 35.6 | 24.8 | 25.3 | 20.3 | 23.6 | **51.6** | | | Codeforces (Percentile) | 17.5 | 35.6 | 24.8 | 25.3 | 20.3 | 23.6 | **51.6** |
| | SWE Verified (Resolved) | - | 22.6 | 23.8 | 24.5 | **50.8** | 38.8 | 42.0 | | | SWE Verified (Resolved) | - | 22.6 | 23.8 | 24.5 | **50.8** | 38.8 | 42.0 |
| | Aider-Edit (Acc.) | 60.3 | 71.6 | 65.4 | 63.9 | **84.2** | 72.9 | 79.7 | | | Aider-Edit (Acc.) | 60.3 | 71.6 | 65.4 | 63.9 | **84.2** | 72.9 | 79.7 |
| | Aider-Polyglot (Acc.) | - | 18.2 | 7.6 | 5.8 | 45.3 | 16.0 | **49.6** | | | Aider-Polyglot (Acc.) | - | 18.2 | 7.6 | 5.8 | 45.3 | 16.0 | **49.6** |
| Math | AIME 2024 (Pass@1) | 4.6 | 16.7 | 23.3 | 23.3 | 16.0 | 9.3 | **39.2** | | Math | AIME 2024 (Pass@1) | 4.6 | 16.7 | 23.3 | 23.3 | 16.0 | 9.3 | **39.2** |
| | MATH-500 (EM) | 56.3 | 74.7 | 80.0 | 73.8 | 78.3 | 74.6 | **90.2** | | | MATH-500 (EM) | 56.3 | 74.7 | 80.0 | 73.8 | 78.3 | 74.6 | **90.2** |
| | CNMO 2024 (Pass@1) | 2.8 | 10.8 | 15.9 | 6.8 | 13.1 | 10.8 | **43.2** | | | CNMO 2024 (Pass@1) | 2.8 | 10.8 | 15.9 | 6.8 | 13.1 | 10.8 | **43.2** |
| Chinese | CLUEWSC (EM) | 89.9 | 90.4 | **91.4** | 84.7 | 85.4 | 87.9 | 90.9 | | Chinese | CLUEWSC (EM) | 89.9 | 90.4 | **91.4** | 84.7 | 85.4 | 87.9 | 90.9 |
| | C-Eval (EM) | 78.6 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 | **86.5** | | | C-Eval (EM) | 78.6 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 | **86.5** |
| | C-SimpleQA (Correct) | 48.5 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 | **64.8** | | | C-SimpleQA (Correct) | 48.5 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 | **64.8** |
</div> </div>
> [!NOTE] > [!NOTE]
> All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models. > All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models.
#### Open Ended Generation Evaluation
#### Open Ended Generation Evaluation
<div align="center"> <div align="center">
| Model | Arena-Hard | AlpacaEval 2.0 |
| ---------------------- | ---------- | -------------- |
| Model | Arena-Hard | AlpacaEval 2.0 | | DeepSeek-V2.5-0905 | 76.2 | 50.5 |
|-------|------------|----------------| | Qwen2.5-72B-Instruct | 81.2 | 49.1 |
| DeepSeek-V2.5-0905 | 76.2 | 50.5 | | LLaMA-3.1 405B | 69.3 | 40.5 |
| Qwen2.5-72B-Instruct | 81.2 | 49.1 | | GPT-4o-0513 | 80.4 | 51.1 |
| LLaMA-3.1 405B | 69.3 | 40.5 | | Claude-Sonnet-3.5-1022 | 85.2 | 52.0 |
| GPT-4o-0513 | 80.4 | 51.1 | | DeepSeek-V3 | **85.5** | **70.0** |
| Claude-Sonnet-3.5-1022 | 85.2 | 52.0 |
| DeepSeek-V3 | **85.5** | **70.0** |
</div> </div>
> [!NOTE] > [!NOTE]
> English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric. > English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
## 5. Chat Website & API Platform ## 5. Chat Website & API Platform
You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in) You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
@ -249,7 +251,7 @@ python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-h
``` ```
> [!NOTE] > [!NOTE]
> Hugging Face's Transformers has not been directly supported yet.** > Hugging Face's Transformers has not been directly supported yet.\*\*
### 6.1 Inference with DeepSeek-Infer Demo (example only) ### 6.1 Inference with DeepSeek-Infer Demo (example only)
@ -259,12 +261,14 @@ python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-h
> Linux with Python 3.10 only. Mac and Windows are not supported. > Linux with Python 3.10 only. Mac and Windows are not supported.
Dependencies: Dependencies:
``` ```
torch==2.4.1 torch==2.4.1
triton==3.0.0 triton==3.0.0
transformers==4.46.3 transformers==4.46.3
safetensors==0.4.5 safetensors==0.4.5
``` ```
#### Model Weights & Demo Code Preparation #### Model Weights & Demo Code Preparation
First, clone our DeepSeek-V3 GitHub repository: First, clone our DeepSeek-V3 GitHub repository:
@ -317,16 +321,15 @@ Multi-Token Prediction (MTP) is in development, and progress can be tracked in t
Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3 Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
### 6.3 Inference with LMDeploy (recommended) ### 6.3 Inference with LMDeploy (recommended)
[LMDeploy](https://github.com/InternLM/lmdeploy), a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows. [LMDeploy](https://github.com/InternLM/lmdeploy), a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows.
For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960 For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960
### 6.4 Inference with TRT-LLM (recommended) ### 6.4 Inference with TRT-LLM (recommended)
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom 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. [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom 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 (recommended) ### 6.5 Inference with vLLM (recommended)
[vLLM](https://github.com/vllm-project/vllm) v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers _pipeline parallelism_ allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the [vLLM instructions](https://docs.vllm.ai/en/latest/serving/distributed_serving.html). Please feel free to follow [the enhancement plan](https://github.com/vllm-project/vllm/issues/11539) as well. [vLLM](https://github.com/vllm-project/vllm) v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers _pipeline parallelism_ allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the [vLLM instructions](https://docs.vllm.ai/en/latest/serving/distributed_serving.html). Please feel free to follow [the enhancement plan](https://github.com/vllm-project/vllm/issues/11539) as well.
@ -336,13 +339,15 @@ For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy
In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the [SGLang instructions](#63-inference-with-lmdeploy-recommended). In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the [SGLang instructions](#63-inference-with-lmdeploy-recommended).
### 6.7 Recommended Inference Functionality with Huawei Ascend NPUs ### 6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The [MindIE](https://www.hiascend.com/en/software/mindie) framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the [instructions here](https://modelers.cn/models/MindIE/deepseekv3). The [MindIE](https://www.hiascend.com/en/software/mindie) framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the [instructions here](https://modelers.cn/models/MindIE/deepseekv3).
## 7. License ## 7. License
This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V3 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V3 series (including Base and Chat) supports commercial use. This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V3 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V3 series (including Base and Chat) supports commercial use.
## 8. Citation ## 8. Citation
``` ```
@misc{deepseekai2024deepseekv3technicalreport, @misc{deepseekai2024deepseekv3technicalreport,
title={DeepSeek-V3 Technical Report}, title={DeepSeek-V3 Technical Report},
@ -356,4 +361,5 @@ This code repository is licensed under [the MIT License](LICENSE-CODE). The use
``` ```
## 9. Contact ## 9. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com). If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).