Introduction
Large Language Models (LLMs) possess remarkable capabilities in generating natural language across a range of tasks, including text summarization, question answering, and conversational agents. However, effectively deploying and implementing these models in real-world applications presents significant challenges. LLMs are often massive, demanding substantial memory and computational resources for efficient operation. Additionally, their performance hinges on the input and output lengths, which can exhibit substantial and unpredictable variations.
To address this predicament, researchers at the University of California, Berkeley, have introduced a novel open-source library that serves as a simpler, faster, and more cost-effective alternative for LLM inference and deployment. The Large Model Systems Organization (LMSYS) is actively leveraging this library to empower their Vicuna and Chatbot Arena initiatives. This groundbreaking library is named 'vLLM', heralding a new era of possibilities.
What is vLLM?
vLLM stands for virtual Large Language Models. It is a library that enables fast and easy LLM inference and serving with HuggingFace Transformers. It utilizes PagedAttention, a new attention algorithm that effectively manages attention key and value tensors in GPU memory vLLM is flexible and easy to use. It seamlessly integrates with popular HuggingFace models without requiring any model architecture changes.
Key Features of vLLM
Some of the key features of vLLM are:
- Unleashing unparalleled serving throughput: Witness the staggering performance of vLLM, delivering up to 24 times greater throughput than HuggingFace Transformers and an outstanding 3.5 times higher throughput than HuggingFace Text Generation Inference (TGI), the previous pinnacle of achievement.
- Efficient mastery of attention key and value memory: Empowered by PagedAttention, vLLM revolutionizes memory storage, ensuring flawless continuity of keys and values within non-contiguous memory space. Say goodbye to memory fragmentation and enjoy a remarkable 60% - 80% reduction in over-reservation.
- Dynamic batching for seamless request management: Experience the optimization prowess of vLLM as it dynamically batches incoming requests based on their input lengths, unlocking the full potential of GPU utilization and enhancing throughput to new heights.
- Unleash high-throughput serving with diverse decoding algorithms: Embrace versatility with vLLM's support for a wide range of decoding algorithms. From parallel sampling and beam search to top-k sampling, explore the perfect fit for your needs. Additionally, unleash your creativity with custom decoding algorithms utilizing user-defined functions.
- Tensor parallelism for distributed inference: Harness the immense power of vLLM's tensor parallelism, enabling distributed inference across multiple GPUs or machines. Conquer the limitations of GPU memory by intelligently splitting larger models into smaller chunks, empowering distributed processing.
- Seamless streaming outputs: Immerse yourself in real-time interactivity as vLLM elegantly streams outputs as they are generated. Say goodbye to latency and embrace a fluid experience. Enjoy the added benefits of output truncation and termination based on your custom criteria.
- Your gateway to OpenAI compatibility: Explore the seamless integration possibilities with vLLM's OpenAI-compatible API server. With support for both HTTP and gRPC protocols, effortlessly integrate with your existing applications and effortlessly handle multiple concurrent requests.
Capabilities/Use Case of vLLM
vLLM offers a wide range of capabilities and finds applications in various tasks that require natural language generation. Some of these include:
- Text Summarization: vLLM excels at generating concise summaries of extensive texts, such as news articles, research papers, books, and more. It leverages powerful models like BART or T5, which are specifically trained for text summarization tasks.
- Question Answering: With its proficiency in understanding natural language, vLLM can generate accurate answers to a diverse range of questions, be it factual, trivia, or open-ended. It harnesses the capabilities of models like GPT-3 or T5, which are designed for question answering tasks.
- Conversational Agents: vLLM empowers conversational agents to deliver natural and engaging responses to user inputs, including queries, commands, or feedback. By utilizing models like GPT-3 or GPT-J, which are specialized in conversational tasks, vLLM enables interactive and meaningful interactions.
- Text Generation: vLLM is an invaluable tool for generating high-quality natural language texts to cater to various needs such as creative writing, content creation, data augmentation, and more. It employs models like GPT-2 or GPT-Neo, which are proficient in general text generation tasks.
Real-World Examples of vLLM Empowered Use Cases
vLLM has powered numerous real-world use cases, showcasing its capabilities and potential impact. Here are a couple of notable examples:
- Vicuna: Vicuna is a cutting-edge web-based platform that enables users to engage with LLMs in diverse ways. Users can explore different models and tasks, including text summarization, question answering, text generation, and more. Additionally, users have the freedom to create and share custom tasks with others. Vicuna utilizes vLLM effectively and affordably, ensuring seamless LLM services.
- Chatbot Arena: Chatbot Arena serves as a powerful web-based platform for users to create and test chatbots using LLMs. It offers a range of models and scenarios to choose from, such as customer service, personal assistant, social chatbot, and more. Users can personalize their chatbots with custom data and logic. By leveraging vLLM, Chatbot Arena delivers efficient and cost-effective LLM services.
How does vLLM work?
vLLM is a library that speeds up the inference of large language models (LLMs) on GPUs. It does this by using PagedAttention, a new attention algorithm that stores key-value tensors more efficiently in the non-contiguous spaces of the GPU VRAM.
PagedAttention is inspired by virtual memory and paging in operating systems. It reduces memory fragmentation and over-reservation by 60% - 80%. It also enables dynamic batching of incoming requests by allowing them to share the same memory space.
To fully take advantage of PagedAttention, vLLM also supports dynamic batching and streaming, which are two other techniques that optimize the GPU utilization and throughput. Dynamic batching batches incoming requests based on their input lengths, minimizing the padding overhead and maximizing the parallelism. Streaming streams outputs as they are generated, reducing latency and improving interactivity.
How to access and use this model?
vLLM is an open-source library that allows you to use HuggingFace models for fast and easy LLM inference and serving. It is licensed under Apache 2.0 and can be accessed from GitHub and ReadTheDocs. It also provides an OpenAI-compatible API server for easy integration with existing applications.To use vLLM, you need to install it from PyPI, load your desired HuggingFace model, and start a vLLM server. Then, you can send requests to the vLLM server with various decoding options and receive outputs in streaming or non-streaming mode. Alternatively, you can use vLLM as a library without starting a server and generate outputs directly from your Python code.
Conclusion
vLLM presents itself as a formidable and versatile instrument, enabling you to harness the true potential of LLMs across diverse applications and tasks in natural language generation. While still in its developmental phase, vLLM shows promising prospects of optimizing the efficiency and accessibility of LLM serving for all users. To delve deeper into this subject, we recommend consulting the documentation or exploring the vLLM GitHub repository.
Source
Documentation - https://vllm.readthedocs.io/en/latest/
Blog Article - https://vllm.ai/
Project details - https://github.com/vllm-project/vllm
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