Jan 22, 2025 · DeepSeek's architecture includes a range of advanced features that distinguish it from other language models. Here's a closer look at the technical elements that make this LLM both efficient and effective.

Jan 23, 2025 · DeepSeek has introduced an innovative approach to improving the reasoning capabilities of large language models (LLMs) through reinforcement learning (RL), detailed in their recent paper on...

On Jan. 20, 2025, DeepSeek released its R1 LLM at a fraction of the cost that other vendors incurred in their own developments. DeepSeek is also providing its R1 models under an open source license, enabling free use.

Feb 6, 2025 · Taken as a whole, the DeepSeek series highlights how architecture, algorithms, frameworks, and hardware must be co-designed to handle LLM training at trillion-token scales.

Jan 25, 2025 · This article provides an in-depth analysis of DeepSeek LLM, focusing on its architecture, training methodologies, reinforcement learning techniques, and performance evaluation across...

Dec 27, 2024 · To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.

To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.

Jan 2, 2025 · Explore how DeepSeek-V3 redefines AI with groundbreaking architecture, efficient training, and impactful real-world applications in coding, education, and multilingual systems. DeepSeek-V3 marks a transformative advancement in the domain of large language models (LLMs), setting a new benchmark for open-source AI.

DeepSeek R1 has about 670 billion parameters, making it the largest open-source LLM yet, according to BBC. DeepSeek’s success with the R1 model is based on several key innovations, Forbes reports, such as heavily relying on reinforcement learning, utilizing a “mixture-of-experts” architecture which allows it to activate only a small ...

DeepSeek-V3 on the Chinese SimpleQA benchmark, primarily due to its tendency to refuse answering certain queries after safety RL. Without safety RL, DeepSeek-R1 could achieve an accuracy of over 70%. DeepSeek-R1 also delivers impressive results on IF-Eval, a benchmark designed to assess a model’s ability to follow format instructions.