llava

name llava description Vision-language chat: VQA, captioning, image dialogue. version 1.0.0 author Orchestra Research license MIT dependencies ["transformers","torch","pillow"] platforms ["linux","macos","windows"] metadata {"hermes":{"tags":["LLaVA","Vision-Language","Multimodal","Visual Question Answering","Image Chat","CLIP","Vicuna","Conversational AI","Instruction Tuning","VQA"]}} 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