Llava Onevision Qwen2 72b Ov Chat Hf
Llava Onevision Qwen2 72b Ov Chat Hf is a multimodal AI model that can handle various computer vision tasks. It's the first single model that can perform well in single-image, multi-image, and video scenarios, and it allows for strong transfer learning across different modalities. This model is efficient and fast, supporting multi-image and multi-prompt generation, and it can be optimized further with 4-bit quantization and Flash-Attention 2. With its unique architecture and capabilities, Llava Onevision Qwen2 72b Ov Chat Hf is a powerful tool for tasks like image-to-text generation and conversation. But how does it work for you? Can it handle your specific use case, and what kind of results can you expect from it?
Table of Contents
Model Overview
Meet the LLaVA-Onevision model, a game-changing AI that can understand and interact with images and videos like never before. This open-source multimodal LLM (Large Language Model) is trained to handle three important computer vision scenarios: single-image, multi-image, and video scenarios.
Capabilities
The LLaVA-Onevision model is a powerful tool that can handle multiple tasks at once. It’s like a superhero of AI models!
What can it do?
- Understand images and videos: It can look at a picture or a video and tell you what’s happening in it.
- Answer questions: You can ask it a question, and it will do its best to give you a correct answer.
- Generate text: It can create text based on what you give it, like a conversation or a story.
- Work with multiple images: You can give it multiple images, and it will understand the relationships between them.
What makes it special?
- Transfer learning: It can learn from one task and apply that knowledge to another task, even if they are different.
- Strong video understanding: It’s really good at understanding videos, which is a challenging task for AI models.
- Cross-scenario capabilities: It can work well in different scenarios, like single-image, multi-image, and video scenarios.
Performance
The LLaVA-Onevision model is a powerhouse when it comes to performance. Let’s dive into its speed, accuracy, and efficiency in various tasks.
Speed
How fast is it? The model is designed to process multiple images and prompts simultaneously, making it incredibly fast. With the ability to use 4-bit quantization and Flash-Attention 2, it can generate text at an impressive speed.
Accuracy
How accurate is it? The LLaVA-Onevision model has demonstrated strong performance in three important computer vision scenarios: single-image, multi-image, and video scenarios. Its ability to transfer learning across different modalities/scenarios yields new emerging capabilities, making it highly accurate.
Efficiency
How efficient is it?
The model is optimized for performance and can be used with various precision options, including bfloat16 and float16. This allows for efficient use of resources, making it a great choice for a wide range of applications.
Real-World Applications
The LLaVA-Onevision model’s impressive performance and efficiency make it a great choice for a wide range of applications, including:
- Image and video analysis
- Text generation
- Multimodal tasks
With its ability to process multiple images and prompts simultaneously, the possibilities are endless!
Limitations
The LLaVA-Onevision model is a powerful multimodal model, but it’s not perfect. Let’s explore some of its limitations.
Limited Domain Knowledge
While the LLaVA-Onevision model has been trained on a vast amount of data, its knowledge in specific domains might be limited. For example, it may not have the same level of expertise as a human doctor or a lawyer.
Dependence on Data Quality
The quality of the data used to train the LLaVA-Onevision model can significantly impact its performance. If the training data contains biases or inaccuracies, the model may learn and replicate these flaws.
Limited Common Sense
The LLaVA-Onevision model is great at understanding language, but it may not always have the same level of common sense as a human. For instance, it might not understand the nuances of human behavior or the implications of certain actions.
Vulnerability to Adversarial Attacks
Like other AI models, the LLaVA-Onevision model can be vulnerable to adversarial attacks, which are designed to manipulate the model’s output. These attacks can be used to make the model produce incorrect or misleading results.
Comparison to Other Models
The LLaVA-Onevision model stands out from other models in its ability to simultaneously process multiple images and prompts, making it a unique and powerful tool. Its strong transfer learning capabilities also set it apart from other models.
Format
The LLaVA-Onevision model is a multimodal AI model that can handle both text and images. It uses a combination of two models: SO400M and Qwen2. This model is special because it can do three important tasks at the same time: single-image, multi-image, and video understanding.
Architecture
The model is trained in four stages:
- Pretraining Stage: The model is first trained on a large dataset of images and text.
- Mid Stage: The model is then trained on a mixture of synthetic data and real-world images.
- Final-Image Stage: The model is trained on a large dataset of single images.
- OneVision Stage: The model is finally trained on a mixture of single-image, multi-image, and video data.
Data Formats
The LLaVA-Onevision model supports the following data formats:
- Text: The model can take in text prompts and generate text outputs.
- Images: The model can take in images and generate text outputs based on the image content.
Special Requirements
- Input: The model requires a specific prompt template to be used. This template includes a chat history with text and image inputs.
- Output: The model generates text outputs based on the input prompt.
Code Examples
Here’s an example of how to use the model with a pipeline:
from transformers import pipeline, AutoProcessor
from PIL import Image
import requests
model_id = "llava-hf/llava-onevision-qwen2-72b-ov-chat-hf"
pipe = pipeline("image-to-text", model=model_id)
processor = AutoProcessor.from_pretrained(model_id)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
And here’s an example of how to use the model with pure transformers:
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-72b-ov-chat-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors="pt").to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
