LLaVA

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LLaVA is an open-source AI Agents & Automation tool that provides visual instruction tuning for large language and vision models. It aims for GPT-4 level capabilities, supporting various models and offering high-throughput serving.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is LLaVA?

LLaVA (Large Language and Vision Assistant) is an open-source project focused on visual instruction tuning to develop large language and vision models with capabilities comparable to GPT-4. It offers improved baselines and supports community contributions, making it a robust platform for multimodal AI research and development. Recent releases include LLaVA-NeXT models with support for LLaMA-3 and Qwen-1.5, LLaVA-NeXT (Video) for zero-shot modality transfer, and LMMs-Eval for efficient evaluation of Large Multimodal Models. The project also provides LLaVA-Plus for multimodal agents and LLaVA-Interactive for human-AI multimodal interaction, including image chat, segmentation, generation, and editing. LLaVA supports LoRA finetuning for reduced GPU RAM and offers various model checkpoints through its Model Zoo.

Best used for

Ideal for developers and data scientists who need to build and experiment with large language and vision models, evaluate LMMs efficiently, and develop multimodal AI agents. Especially valuable for researchers and practitioners aiming for GPT-4 level capabilities in visual instruction tuning and multimodal interaction.

Common actions

build multimodal models

evaluate LMMs

finetune vision models

develop AI agents

process visual instructions

automated workflowworkflowsopen-sourcedeepfakecollaborationlow-code/no-codeface swappinggithub copilot"AI Agents"

Capabilities

Key features

Visual instruction tuning
GPT-4 level capabilities
LLaVA-NeXT models
LMMs-Eval pipeline
LoRA finetuning support
Multimodal agents
Image chat/editing

Target Audience

developerdata scientiststartup founder

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running LLaVA?

LLaVA primarily supports Linux environments for installation and operation. While community contributions exist for macOS and Windows, the official documentation recommends Linux. Specific hardware requirements, especially GPU VRAM, depend on the model size and whether quantization (e.g., 4-bit) is used.

How can I serve LLaVA models for high-throughput inference?

For high-throughput serving, LLaVA recommends using SGLang. This involves launching an SGLang backend worker to execute models on GPUs and then a LLaVA-SGLang worker to route requests between the LLaVA controller and the SGLang backend.

Can I finetune LLaVA models on my own datasets?

Yes, LLaVA supports finetuning on custom datasets. The project provides documentation and scripts for finetuning LLaVA-1.5 using LoRA, which helps reduce GPU RAM requirements compared to full-model finetuning.

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Coding & Development › Open Source & Models

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