What is LLaVA and how does it work with Claude Code?

LLaVA is a Large Language and Vision Assistant that integrates image processing with language models. This skill allows your Claude agent to analyze images and hold conversations about visual content using open-source models.

Is a GPU required for this LLaVA skill?

Yes, a GPU is strongly recommended. While CPU inference is technically possible via underlying libraries, a GPU is required for the responsive performance needed in conversational AI applications.

How does LLaVA compare to GPT-4V?

While GPT-4V is a proprietary API, LLaVA is an open-source alternative that offers competitive performance on benchmarks like VQAv2 and MMBench with the benefit of local deployment and data privacy.

Does this skill support document analysis?

Yes, LLaVA is highly effective for document understanding, enabling you to ask questions about text, charts, and structure within image-based documents or PDFs.

Can I run LLaVA on consumer hardware?

Yes, the skill supports 4-bit and 8-bit quantization, allowing the 7B and 13B models to run on GPUs with as little as 4GB to 8GB of VRAM.

LLaVA Multimodal Assistant

Name: LLaVA Multimodal Assistant
Author: zechenzhangAGI

byzechenzhangAGI

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384

データサイエンスとML

Enables advanced vision-language capabilities for image understanding, multi-turn visual conversations, and document analysis.

概要

LLaVA (Large Language and Vision Assistant) is a state-of-the-art open-source multimodal model that combines a CLIP vision encoder with LLaMA/Vicuna language models to bridge the gap between vision and language. This skill provides a comprehensive toolkit for implementing visual instruction tuning, visual question answering (VQA), and detailed image captioning within your AI workflows. It is particularly useful for developers building AI research agents, conversational image analysis tools, or complex document understanding systems that require both visual reasoning and natural language generation.

主な機能

Multi-turn image-based conversational AI
384 GitHub stars
Visual instruction following and scene understanding
High-accuracy Visual Question Answering (VQA)
Support for 4-bit and 8-bit quantization for VRAM efficiency
Seamless integration with Gradio and LangChain frameworks

ユースケース

Automating document data extraction and textual scene analysis
Developing AI research agents with multimodal perception capabilities
Building vision-enabled chatbots for customer support or education

概要

主な機能

Multi-turn image-based conversational AI
384 GitHub stars
Visual instruction following and scene understanding
High-accuracy Visual Question Answering (VQA)
Support for 4-bit and 8-bit quantization for VRAM efficiency
Seamless integration with Gradio and LangChain frameworks

ユースケース

Automating document data extraction and textual scene analysis
Developing AI research agents with multimodal perception capabilities
Building vision-enabled chatbots for customer support or education