Llava Agent Skill

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.

3k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

11362

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill llava

The instruction itself

25 sections, as written by the author

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

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

# Install
pip install -e .

Basic usage

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)

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images=image_tensor,
        do_sample=True,
        temperature=0.2,
        max_new_tokens=512
    )

response = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
print(response)

Available models

|-------|------------|------|---------|

| LLaVA-v1.5-7B | 7B | ~14 GB | Good |

| LLaVA-v1.5-13B | 13B | ~28 GB | Better |

| LLaVA-v1.6-34B | 34B | ~70 GB | Best |

# Load different models
model_7b = "liuhaotian/llava-v1.5-7b"
model_13b = "liuhaotian/llava-v1.5-13b"
model_34b = "liuhaotian/llava-v1.6-34b"

# 4-bit quantization for lower VRAM
load_4bit = True  # Reduces VRAM by ~4×

CLI usage

# Single image query
python -m llava.serve.cli \
    --model-path liuhaotian/llava-v1.5-7b \
    --image-file image.jpg \
    --query "What is in this image?"

# Multi-turn conversation
python -m llava.serve.cli \
    --model-path liuhaotian/llava-v1.5-7b \
    --image-file image.jpg
# Then type questions interactively

Web UI (Gradio)

# Launch Gradio interface
python -m llava.serve.gradio_web_server \
    --model-path liuhaotian/llava-v1.5-7b \
    --load-4bit  # Optional: reduce VRAM

# Access at http://localhost:7860

Multi-turn conversations

# Initialize conversation
conv = conv_templates["llava_v1"].copy()

# Turn 1
conv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + "\nWhat is in this image?")
conv.append_message(conv.roles[1], None)
response1 = generate(conv, model, image)  # "A dog playing in a park"

# Turn 2
conv.messages[-1][1] = response1  # Add previous response
conv.append_message(conv.roles[0], "What breed is the dog?")
conv.append_message(conv.roles[1], None)
response2 = generate(conv, model, image)  # "Golden Retriever"

# Turn 3
conv.messages[-1][1] = response2
conv.append_message(conv.roles[0], "What time of day is it?")
conv.append_message(conv.roles[1], None)
response3 = generate(conv, model, image)

Common tasks

Image captioning

question = "Describe this image in detail."
response = ask(model, image, question)

Visual question answering

question = "How many people are in the image?"
response = ask(model, image, question)

Object detection (textual)

question = "List all the objects you can see in this image."
response = ask(model, image, question)

Scene understanding

question = "What is happening in this scene?"
response = ask(model, image, question)

Document understanding

question = "What is the main topic of this document?"
response = ask(model, document_image, question)

Training custom model

# Stage 1: Feature alignment (558K image-caption pairs)
bash scripts/v1_5/pretrain.sh

# Stage 2: Visual instruction tuning (150K instruction data)
bash scripts/v1_5/finetune.sh

Quantization (reduce VRAM)

# 4-bit quantization
tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path="liuhaotian/llava-v1.5-13b",
    model_base=None,
    model_name=get_model_name_from_path("liuhaotian/llava-v1.5-13b"),
    load_4bit=True  # Reduces VRAM ~4×
)

# 8-bit quantization
load_8bit=True  # Reduces VRAM ~2×

Best practices

Start with 7B model - Good quality, manageable VRAM
Use 4-bit quantization - Reduces VRAM significantly
GPU required - CPU inference extremely slow
Clear prompts - Specific questions get better answers
Multi-turn conversations - Maintain conversation context
Temperature 0.2-0.7 - Balance creativity/consistency
max_new_tokens 512-1024 - For detailed responses
Batch processing - Process multiple images sequentially

Performance

|-------|-------------|--------------|------------------|

| 7B | ~14 GB | ~4 GB | ~20 |

| 13B | ~28 GB | ~8 GB | ~12 |

| 34B | ~70 GB | ~18 GB | ~5 |

*On A100 GPU*

Benchmarks

LLaVA achieves competitive scores on:

VQAv2: 78.5%
GQA: 62.0%
MM-Vet: 35.4%
MMBench: 64.3%

Limitations

Hallucinations - May describe things not in image
Spatial reasoning - Struggles with precise locations
Small text - Difficulty reading fine print
Object counting - Imprecise for many objects
VRAM requirements - Need powerful GPU
Inference speed - Slower than CLIP

Integration with frameworks

LangChain

from langchain.llms.base import LLM

class LLaVALLM(LLM):
    def _call(self, prompt, stop=None):
        # Custom LLaVA inference
        return response

llm = LLaVALLM()

Gradio App

import gradio as gr

def chat(image, text, history):
    response = ask_llava(model, image, text)
    return response

demo = gr.ChatInterface(
    chat,
    additional_inputs=[gr.Image(type="pil")],
    title="LLaVA Chat"
)
demo.launch()

Resources

GitHub: https://github.com/haotian-liu/LLaVA ⭐ 23,000+
Paper: https://arxiv.org/abs/2304.08485
Demo: https://llava.hliu.cc
Models: https://huggingface.co/liuhaotian
License: Apache 2.0

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13k tokens

How to use it

Copy the folder

Take orchestra-research/llava from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.