
Tumbabikesandblooms
FollowVue d'ensemble
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Date de fondation juillet 3, 2019
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Secteurs BTP / Immobilier / Urbanisme
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Emplois publiés 0
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Vu 13
Description de l'entreprise
GitHub – Deepseek-ai/DeepSeek-V3
We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total criteria with 37B activated for each token. To accomplish effective reasoning and affordable training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training goal for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to fully harness its abilities. Comprehensive evaluations expose that DeepSeek-V3 exceeds other open-source designs and accomplishes performance similar to leading closed-source designs. Despite its exceptional efficiency, DeepSeek-V3 requires just 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is extremely 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 pioneer an auxiliary-loss-free technique for load balancing, which reduces the performance destruction that occurs from motivating load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it helpful to design performance. It can likewise be utilized for speculative decoding for inference velocity.
Pre-Training: Towards Ultimate Training Efficiency
– We create an FP8 combined accuracy training structure and, for the very first time, validate the expediency and effectiveness of FP8 training on an extremely massive model.
– Through co-design of algorithms, frameworks, and hardware, we overcome the interaction bottleneck in cross-node MoE training, nearly accomplishing full computation-communication overlap.
This substantially improves our training performance and minimizes the training expenses, enabling us to further scale up the design size without additional overhead.
– At an economical expense of only 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 model. The subsequent training stages after pre-training require only 0.1 M GPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
– We present an ingenious approach to distill thinking capabilities from the long-Chain-of-Thought (CoT) design, specifically from one of the DeepSeek R1 series designs, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and notably enhances its thinking efficiency. Meanwhile, we likewise 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, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To make sure optimum performance and flexibility, we have actually partnered with open-source neighborhoods and hardware suppliers to supply several methods to run the model locally. For detailed guidance, take a look at Section 6: How_to Run_Locally.
For designers looking to dive 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 assistance 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 strong. Scores with a space not exceeding 0.3 are thought about to be at the same level. DeepSeek-V3 accomplishes the very best performance on a lot of criteria, especially on math and code tasks. For more assessment information, please examine our paper.
Context Window
Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well throughout all context window lengths approximately 128K.
Chat Model
Standard Benchmarks (Models larger than 67B)
All designs are assessed in a configuration that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are checked several times utilizing varying temperature level settings to obtain robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise exhibits competitive efficiency versus frontier closed-source models.
Open Ended Generation Evaluation
English open-ended discussion assessments. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
5. Chat Website & API Platform
You can chat with DeepSeek-V3 on DeepSeek’s main 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 deployed locally utilizing the following hardware and open-source community software:
DeepSeek-Infer Demo: We offer an easy and lightweight demo for FP8 and BF16 reasoning.
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 support 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 model 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 embraced in our structure, we only offer FP8 weights. If you need BF16 weights for experimentation, you can utilize the offered conversion script to perform the improvement.
Here is an example of converting FP8 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 just. 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 install reliances noted in requirements.txt. Easiest way is to use a package manager like conda or uv to create a 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 design weights to a specific format:
Run
Then you can talk with DeepSeek-V3:
Or batch reasoning on an offered file:
6.2 Inference with SGLang (suggested)
SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing state-of-the-art 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 flexible and robust solution.
SGLang also supports multi-node tensor parallelism, enabling you to run this model on multiple network-connected machines.
Multi-Token Prediction (MTP) remains in advancement, and development can be tracked in the optimization strategy.
Here are the launch guidelines 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 large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation abilities, effortlessly incorporating with PyTorch-based workflows.
For comprehensive detailed directions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (suggested)
TensorRT-LLM now supports the DeepSeek-V3 model, providing precision alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 assistance through the following link to experience the new functions 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 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard strategies, vLLM uses pipeline parallelism allowing you to run this model on numerous devices linked by networks. For in-depth assistance, please refer to the vLLM directions. Please feel totally free to follow the enhancement plan also.
6.6 Recommended Inference Functionality with AMD GPUs
In collaboration with the AMD group, we have attained 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 directions.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE framework from the Huawei Ascend neighborhood has effectively adapted the BF16 version of DeepSeek-V3. For detailed guidance on Ascend NPUs, please follow the directions here.
7. License
This code repository is accredited under the MIT License. Using DeepSeek-V3 Base/Chat models undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.