# llava

A skill that wires LLaVA into your agent: ask questions about an image and keep the conversation going. Useful for image chat and document scans.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 940 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/18-multimodal/llava/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/llava

## What it does

The skill documents how to run LLaVA, an open vision-language model that pairs a CLIP vision encoder with Vicuna and LLaMA language models. Inside are recipes for loading weights with `load_pretrained_model`, preprocessing images through `process_images`, building a prompt from `conv_templates["llava_v1"]`, and generating a reply. There are examples for the CLI, a Gradio interface, and multi-turn chats where the previous answer is fed back into the history.

Models come in 7B, 13B and 34B sizes. The 7B model needs roughly 14 GB of VRAM in FP16, the 34B one about 70 GB, so check your card before you start. 4-bit quantization cuts VRAM by about 4×, 8-bit by 2×. CPU inference is extremely slow, which makes a GPU the practical requirement here. Dependencies are `transformers`, `torch` and `pillow`.

The skill is upfront about weak spots: the model may describe things that are not in the image, struggles with precise spatial relations, has trouble reading fine print, and counts objects imprecisely when there are many. For plain classification without dialogue, CLIP is simpler; for captioning alone, BLIP-2 does better. If you want top quality through an API, look at GPT-4V.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/llava/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/llava/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/llava/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/llava/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs llava
```

2. **Manually: put the files in place**
- File: `.github/skills/llava/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 18-multimodal/llava
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/llava/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/18-multimodal/llava/SKILL.md

## When to use it
- ask a question about a photo and get a text answer
- build a chatbot that discusses an image over several turns
- describe what an image contains in words
- read a scanned document and pull out its topic
- list the objects visible in a frame

## Example prompts
- Load LLaVA-v1.5-7B and tell me what is in this image
- Set up a multi-turn conversation over `image.jpg`: describe it first, then answer follow-ups
- Run `python -m llava.serve.cli --model-path liuhaotian/llava-v1.5-7b --image-file image.jpg` and ask about the scene

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 83a77f9a42da8164.

BEGIN THIRD-PARTY FILE 83a77f9a42da8164
````text
---
name: llava
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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA]
dependencies: [transformers, torch, pillow]
---

# 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
````
END THIRD-PARTY FILE 83a77f9a42da8164

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