Topic #10: 오픈소스 LLM 씬의 라이징 스타! 'DeepSeek'을 알아보자

DeepSeek hasn’t released the complete value of training R1, however it's charging individuals using its interface round one-thirtieth of what o1 prices to run. Experts estimate that it cost round $6 million to rent the hardware needed to practice the model, compared with upwards of $60 million for Meta’s Llama 3.1 405B, which used eleven times the computing resources. This overlap ensures that, as the mannequin additional scales up, so long as we maintain a continuing computation-to-communication ratio, we will still employ fine-grained specialists across nodes while achieving a close to-zero all-to-all communication overhead. These two architectures have been validated in DeepSeek-V2 (DeepSeek-AI, 2024c), demonstrating their functionality to keep up robust mannequin efficiency whereas achieving environment friendly training and inference. Beyond closed-supply fashions, open-source fashions, together with DeepSeek sequence (DeepSeek-AI, 2024b, c; Guo et al., 2024; DeepSeek-AI, 2024a), LLaMA series (Touvron et al., 2023a, b; AI@Meta, 2024a, b), Qwen sequence (Qwen, 2023, 2024a, 2024b), and Mistral collection (Jiang et al., 2023; Mistral, 2024), are additionally making important strides, endeavoring to close the gap with their closed-source counterparts. Therefore, by way of structure, DeepSeek-V3 still adopts Multi-head Latent Attention (MLA) (DeepSeek-AI, 2024c) for efficient inference and DeepSeekMoE (Dai et al., 2024) for price-efficient training.

Firstly, DeepSeek-V3 pioneers an auxiliary-loss-free technique (Wang et al., 2024a) for load balancing, with the purpose of minimizing the opposed influence on model performance that arises from the hassle to encourage load balancing. Secondly, DeepSeek-V3 employs a multi-token prediction training goal, which we have noticed to boost the overall efficiency on analysis benchmarks. Alternatively, MTP may allow the model to pre-plan its representations for higher prediction of future tokens. The mannequin structure is essentially the identical as V2 with the addition of multi-token prediction, which (optionally) decodes further tokens faster but less accurately. It is not so much a factor we've architected as an impenetrable artifact that we can only take a look at for effectiveness and security, a lot the identical as pharmaceutical merchandise. Another stunning factor is that DeepSeek small fashions usually outperform numerous bigger models. DeepSeek-V2, a common-function text- and image-analyzing system, carried out nicely in various AI benchmarks - and was far cheaper to run than comparable models at the time. Its chat model also outperforms different open-supply models and achieves performance comparable to main closed-source fashions, together with GPT-4o and Claude-3.5-Sonnet, on a series of standard and open-ended benchmarks.

We consider DeepSeek-V3 on a complete array of benchmarks. During pre-coaching, we train DeepSeek-V3 on 14.8T excessive-high quality and numerous tokens. For the MoE all-to-all communication, we use the identical methodology as in coaching: first transferring tokens throughout nodes via IB, and then forwarding among the many intra-node GPUs through NVLink. These GPUs are interconnected using a mix of NVLink and NVSwitch applied sciences, guaranteeing efficient information switch within nodes. As well as, we additionally develop efficient cross-node all-to-all communication kernels to completely utilize InfiniBand (IB) and NVLink bandwidths. Their product allows programmers to more easily combine numerous communication strategies into their software program and applications. As for the training framework, we design the DualPipe algorithm for efficient pipeline parallelism, which has fewer pipeline bubbles and hides many of the communication during training via computation-communication overlap. We introduce our pipeline to develop DeepSeek-R1. Throughout the publish-coaching stage, we distill the reasoning capability from the DeepSeek-R1 collection of models, and meanwhile carefully maintain the stability between model accuracy and technology size. In the primary stage, the utmost context length is extended to 32K, and within the second stage, it is additional prolonged to 128K. Following this, we conduct post-training, including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on the bottom mannequin of DeepSeek-V3, to align it with human preferences and further unlock its potential.

Combined with 119K GPU hours for the context size extension and 5K GPU hours for post-coaching, DeepSeek-V3 prices only 2.788M GPU hours for its full training. Handling lengthy contexts: DeepSeek-Coder-V2 extends the context length from 16,000 to 128,000 tokens, permitting it to work with a lot bigger and extra complicated tasks. Next, we conduct a two-stage context size extension for DeepSeek-V3. Our experiments reveal an interesting trade-off: the distillation leads to better efficiency but in addition considerably will increase the common response length. We pre-trained DeepSeek language fashions on an enormous dataset of 2 trillion tokens, with a sequence size of 4096 and AdamW optimizer. LLMs train on billions of samples of text, snipping them into word-parts, called tokens, and learning patterns in the information. We use CoT and non-CoT strategies to guage mannequin efficiency on LiveCodeBench, the place the information are collected from August 2024 to November 2024. The Codeforces dataset is measured using the proportion of opponents. Very similar to Washington's fears about TikTok, which prompted Congress to ban the app in the U.S., the concern is that a China-based company will finally be answerable to the federal government, doubtlessly exposing Americans' delicate information to an adversarial nation.

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