# Llava

Large Language and Vision Assistant.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Media
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# LLaVA - Large Language and Vision Assistant

Open-source vision-language model for conversational image understanding.

## When to use LLaVA

**Use when:**
- Building vision-language chatbots
- Visual question answering (VQA)
- Image description and captioning
- Multi-turn image conversations
- Visual instruction following
- Document understanding with images

**Metrics**:
- **23,000+ GitHub stars**
- GPT-4V level capabilities (targeted)
- Apache 2.0 License
- Multiple model sizes (7B-34B params)

**Use alternatives instead**:
- **GPT-4V**: Highest quality, API-based
- **CLIP**: Simple zero-shot classification
- **BLIP-2**: Better for captioning only
- **Flamingo**: Research, not open-source

## Quick start

### Installation

```bash
# Clone repository
git clone https://github.com/haotian-liu/LLaVA
cd LLaVA

# Install
pip install -e .
```

### Basic usage

```python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates
from PIL import Image
import torch

# Load model
model_path = "liuhaotian/llava-v1.5-7b"
tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path=model_path,
    model_base=None,
    model_name=get_model_name_from_path(model_path)
)

# Load image
image = Image.open("image.jpg")
image_tensor = process_images([image], image_processor, model.config)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)

# Create conversation
conv = conv_templates["llava_v1"].copy()
conv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + "\nWhat is in this image?")
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()

# Generate response
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/18-multimodal/llava/SKILL.md