Llava

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 metadata

Source	Optional — install with kopi skills install official/mlops/llava
Path	optional-skills/mlops/llava
Version	1.0.0
Author	Orchestra Research
License	MIT
Dependencies	transformers, torch, pillow
Platforms	linux, macos, windows
Tags	LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA

Reference: full SKILL.md

info

The following is the complete skill definition that Kopi Agent loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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

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

• 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