Llava Onevision Qwen2 72b Ov Sft
The LLaVA-OneVision Qwen2 72b Ov Sft model is designed to interact with images, multi-image, and videos. This 72B parameter model, trained on the LLaVA-OneVision dataset, is built on the Qwen2 language model and has a context window of 32K tokens. But what makes it unique? It can process and understand visual data, allowing it to have conversations that go beyond just text. The model's architecture is based on SO400M + Qwen2 and was trained on a combination of synthetic and real-world data. The result is a model that can provide fast and accurate results, making it a valuable tool for those looking to explore the intersection of language and vision. With its ability to handle a wide range of tasks, from image analysis to conversation, the LLaVA-OneVision Qwen2 72b Ov Sft model is a powerful tool for anyone looking to push the boundaries of AI.
Table of Contents
Model Overview
The LLaVA-OneVision model is a powerful AI tool that can interact with images, multi-image, and videos. It’s like having a conversation with a friend who can understand and respond to visual information.
Capabilities
What can it do?
- Understand and respond to images, multi-image, and videos
- Have conversations about visual information
- Generate text based on visual inputs
How does it work?
The model uses a special type of architecture called SO400M + Qwen2. It was trained on a large dataset of images, videos, and text, which allows it to understand the relationships between them.
What makes it unique?
- Large context window: The model can understand a large amount of text and images at once, which allows it to have more in-depth conversations.
- Specialized training: The model was trained on a unique dataset that includes a mix of images, videos, and text, which allows it to understand the relationships between them.
Performance
Speed
How fast can the model process images and videos? The answer is: very fast! With a context window of 32K tokens, this model can handle a large amount of visual data quickly.
Accuracy
But speed isn’t everything. What about accuracy? The model delivers here too. With a precision of bfloat16, this model can provide highly accurate results when interacting with images and videos.
Efficiency
Efficiency is also crucial when it comes to AI models. The model is trained on a range of hardware and software, including 256 Nvidia Tesla A100 GPUs and PyTorch. This means it can be deployed in a variety of environments, making it a versatile and efficient model.
| Task | Performance |
|---|---|
| Image classification | High accuracy with bfloat16 precision |
| Video analysis | Rapid processing with 32K token context window |
| Text generation | Reliable and precise results |
Real-World Applications
So, what can the model be used for? Here are a few examples:
- Image and video analysis
- Text generation based on visual data
- Conversational AI applications
Example Use Case
Here’s an example of how to use the model to generate text output based on an input image:
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
image_tensor = process_images([image], image_processor, model.config)
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
conv_template = "qwen_1_5"
question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
image_sizes = [image.size]
cont = model.generate(input_ids, images=image_tensor, image_sizes=image_sizes, do_sample=False, temperature=0, max_new_tokens=4096)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
print(text_outputs)
This example shows how to preprocess an image, generate text output, and print the result.
Limitations
The model is not perfect. Let’s talk about some of its limitations.
Limited Context Window
The model has a context window of 32K tokens. This means it can only understand and respond to text and images within a certain limit. If the input is too long or complex, the model might struggle to understand it.
Language Limitations
The model is primarily trained on English and Chinese languages. While it can understand some other languages, its performance might not be as good. If you’re using the model with other languages, you might encounter some issues.
Image Understanding
The model is designed to interact with images, multi-image, and videos. However, its understanding of images is limited to the data it was trained on. If the image is too complex or abstract, the model might not be able to understand it correctly.
Dependence on Training Data
The model’s performance is highly dependent on the quality and diversity of the training data. If the training data is biased or limited, the model’s responses might reflect those biases.
Technical Requirements
To use the model, you need to have a good understanding of programming languages like Python and PyTorch. You also need to have a powerful GPU to run the model efficiently.
Conclusion
The model is a powerful tool with many applications. However, it’s essential to understand its limitations and use it accordingly. By being aware of these limitations, you can use the model more effectively and get the most out of it.
