Our overall network architecture is similar to LLaVA-1.5. We use the pre-trained CLIP ViT-L/14 with a resolution of 336x336

vLLM is effective in distributed settings by supporting the widely used Megatron-LM style tensor model parallelism strategy on Transformers

In vLLM, we adopt the first-come-first-serve (FCFS) scheduling policy for all requests, ensuring fairness and preventing starvation.

The rapid advancements in Large Language Models (LLMs) have significantly propelled the development of vision-language models based on LLMs.

We show the general applicability of vLLM on them in this section.

We evaluate the performance of vLLM with basic sampling (one sample per request) on three models and two datasets.

In this section, we evaluate the performance of vLLM under a variety of workloads.

We introduce LLaVA-Phi, a vision language assistant developed using the compact language model Phi-2.

We present several examples that demonstrate the remarkable generalization capabilities of LLaVA-Phi, comparing its outputs with those of the LLaVA-1.5-13B

We rigorously evaluated LLaVA-Phi using an extensive array of academic benchmarks specifically designed for multi-modal models.

Try Llama 3.1 8B in Your Browser: AQLM.rs Delivers Al at Your Fingertips

We evaluate the effectiveness of memory sharing in PagedAttention with two popular sampling methods: parallel sampling and beam search.

To implement a chatbot, we let the model generate a response by concatenating the chatting history and the last user query into a prompt.

We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts

This paper proposes PagedAttention, a new attention algorithm that allows attention keys and values to be stored in non-contiguous paged memory

Model serving has been an active area of research in recent years, with numerous systems proposed to tackle diverse aspects of deep learning model deployment.

The idea of virtual memory and paging is effective for managing the KV cache in LLM serving because the workload requires dynamic memory allocation

In this section, we study various aspects of vLLM and evaluate the design choices we make with ablation experiments.

WLTech.AI explores the ARC challenge, an important benchmark in AI research, advancing the quest for artificial general intelligence through generalization.

Integrating Large Language Models with diagramming tools like Mermaid and UML is revolutionizing software development.