SGLang v0.2

Through our operational experiences and in-depth research, we’ve continuously enhanced the underlying serving systems for the Chatbot Arena platform, spanning from the high-level multi-model serving framework, FastChat, to the efficient serving engine, SGLang Runtime (SRT). This post focuses on SGLang Runtime, a general-purpose serving engine for LLMs and VLMs. While existing options like TensorRT-LLM, vLLM, MLC-LLM, and Hugging Face TGI have their merits, we found them sometimes hard to use, difficult to customize, or lacking in performance.

DeepSeek-Coder-V2

We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks.

DeepSeek-V2

We introduce DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to more than 5 times.

LLM Course

The LLM course is divided into three parts: 🧩 LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks. 🧑‍🔬 The LLM Scientist focuses on building the best possible LLMs using the latest techniques. 👷 The LLM Engineer focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two LLM assistants that will answer questions and test your knowledge in a personalized way.

Awesome LLM

Large Language Models (LLMs) have taken the Whole World by storm. Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM, courses and tutorials about LLM and all publicly available LLM checkpoints and APIs.

DeepSpeed-FastGen Performance and Feature Enhancements

DeepSpeed-FastGen is an inference system framework that enables easy, fast, and affordable inference for large language models (LLMs). From general chat models to document summarization, and from autonomous driving to copilots at every layer of the software stack, the demand to deploy and serve these models at scale has skyrocketed. DeepSpeed-FastGen utilizes the Dynamic SplitFuse technique to tackle the unique challenges of serving these applications and offer higher effective throughput than other state-of-the-art systems.

Fast and Expressive LLM Inference with RadixAttention and SGLang

Large Language Models (LLMs) are increasingly utilized for complex tasks that require multiple chained generation calls, advanced prompting techniques, control flow, and interaction with external environments. However, there is a notable deficiency in efficient systems for programming and executing these applications. To address this gap, we introduce SGLang, a Structured Generation Language for LLMs.

DeepSeek MoE

DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. It employs an innovative MoE architecture, which involves two principal strategies: fine-grained expert segmentation and shared experts isolation. It is trained from scratch on 2T English and Chinese tokens, and exhibits comparable performance with DeekSeek 7B and LLaMA2 7B, with only about 40% of computations.