Awesome-LLM-Inference

Project Introduction: Awesome-LLM-Inference

The Awesome-LLM-Inference project serves as a meticulously curated repository of resources related to large language model (LLM) inference. It includes a comprehensive collection of research papers and corresponding code, aimed at facilitating advancements and understanding in the field of LLM inference. The project is open-source and maintains a focus on providing quality resources for both beginners and experts interested in LLM algorithms and frameworks.

📒Overview

The primary objective of Awesome-LLM-Inference is to gather and present a list of outstanding papers and resources that focus on the various aspects of LLM inference. This project acts as a valuable tool for researchers, educators, and practitioners who wish to dive into or further delve into the innovations in LLMs.

📙Key Features

Awesome LLM Inference Papers with Codes

The repository hosts numerous papers, complete with downloadable PDFs and associated code, allowing users to explore cutting-edge research in LLM inference. These resources are critical for those studying new methods and frameworks for efficient and effective LLM deployment.

Downloadable Resources

One of the highlights of the project is its beginner-friendly PDF guide titled "Awesome LLM Inference for Beginners", which encompasses a broad spectrum of topics including FlashAttention, FlexGen, Continuous Batching, and many more. This guide provides a solid foundation for those new to the field.

📖Detailed Content List

The project is organized into diverse categories, each focusing on specific topics within LLM inference. Some of the notable categories include:

• Trending LLM/VLM Topics: Features current and trending topics in large volume and language models.
• LLM Algorithmic/Eval Survey: Surveys that assess the algorithms and evaluations involved in LLMs.
• LLM Train/Inference Framework/Design: Insights into different frameworks and designs for LLM training and inference.

Each topic in the list is accompanied by relevant papers, resources, and recommended reading levels, making it easy for users to navigate and access the material they need.

🎉Highlights and Recommendations

• Trending Innovations: Gain insights into evolving technologies and practices in efficient video production and LLM serving architectures.
• Best Practices and Frameworks: Learn about algorithmic advances and performance evaluations crucial for LLM developments.
• Diverse Technological Platforms: Explore cutting-edge work involving everything from CPU and GPU to FPGA and Mobile inference solutions.

📒Community and Contributions

The project invites contributions from the community, offering researchers the opportunity to share their work and input related to LLM inference. By integrating and coalescing efforts from various contributors, Awesome-LLM-Inference thrives as a dynamic and evolving resource for the LLM community at large.

❤️Community Engagement

Join the growing community of researchers and practitioners by contributing to the repository or by using the resources for your research and development projects. The project's GitHub page provides all necessary details and contribution guidelines to help you get started.

Conclusively, Awesome-LLM-Inference is an invaluable resource that encapsulates a wealth of knowledge and innovations within the field of LLM inference. It stands as a beacon of collaborative engagement and a testament to the thriving research community.