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llava

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Updated 10-21 16:24

About this skill

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:

  • 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

Model Parameters VRAM Quality
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

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

Performance

Model VRAM (FP16) VRAM (4-bit) Speed (tokens/s)
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

  1. Hallucinations - May describe things not in image
  2. Spatial reasoning - Struggles with precise locations
  3. Small text - Difficulty reading fine print
  4. Object counting - Imprecise for many objects
  5. VRAM requirements - Need powerful GPU
  6. 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

Agent operating procedure

  1. Check the environment. Confirm GPU memory, model checkpoint and licence, framework versions and input formats.
  2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
  3. Run a small version first. Run inference on a single sample at low resolution or short length first.
  4. Execute the full task using the instructions and references above.
  5. Validate the result. Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.
  6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.
If this happens Do this
Out-of-memory on large inputs Reduce resolution or sequence length, use half precision, or tile the input.
A function, flag or endpoint in these instructions is missing in the installed version Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous Ask the user, or state the assumption explicitly before running.

Integrity rules

  • Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
  • Check model and dataset licences before commercial or published use.
  • Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
  • Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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