llava

仓库: davila7/claude-code-templates
安装量: 155
排名: #5555

安装

npx skills add https://github.com/davila7/claude-code-templates --skill llava

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 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 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 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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