Qwen2 VL 72B Instruct AWQ
Qwen2 VL 72B Instruct AWQ is a powerful AI model designed for efficient and accurate visual understanding and generation. What makes it unique is its ability to handle images and videos of varying resolutions and lengths, including videos over 20 minutes. It can also operate devices like mobile phones and robots based on visual environment and text instructions. With multilingual support for several languages, it's a versatile tool for global users. The model's architecture updates, including Naive Dynamic Resolution and Multimodal Rotary Position Embedding, enhance its visual processing and multimodal capabilities. While it has some limitations, such as lack of audio support and limited capacity for complex instructions, it's a remarkable model that can be used for various tasks like image and video understanding, content creation, and more. Its performance is impressive, with state-of-the-art results on visual understanding benchmarks and fast inference speeds. Overall, Qwen2 VL 72B Instruct AWQ is a cutting-edge AI model that's worth exploring for its capabilities and potential applications.
Table of Contents
Model Overview
The Qwen2-VL-72B-Instruct-AWQ model is a state-of-the-art AI model that can understand and process visual information from images and videos. It’s like a super smart robot that can look at pictures and videos and answer questions about them.
Key Features:
- Multimodal Understanding: The model can understand both images and videos, and even process text instructions to operate devices like mobile phones and robots.
- High-Quality Video Understanding: It can understand videos up to 20 minutes long, making it perfect for video-based question answering, dialog, and content creation.
- Multilingual Support: The model supports multiple languages, including English, Chinese, and many European languages, Japanese, Korean, Arabic, and Vietnamese.
- Dynamic Resolution: It can handle images of various resolutions and ratios, making it more human-like in its visual processing.
Capabilities
The Qwen2-VL-72B-Instruct-AWQ model is a powerful tool that can understand and process visual information from images and videos. It’s like having a super smart assistant that can look at pictures and videos and answer your questions about them.
What can it do?
- Understand images and videos: It can look at images and videos and understand what’s going on in them. It can recognize objects, people, and actions, and even answer questions about what it sees.
- Answer questions: You can ask it questions about an image or video, and it will do its best to answer them. For example, you could ask it to describe what’s happening in a picture, or to identify specific objects or people.
- Generate text: It can also generate text based on what it sees in an image or video. For example, you could ask it to write a caption for a picture, or to summarize what’s happening in a video.
- Multilingual support: It can understand and process text in multiple languages, including English, Chinese, and many others.
How does it work?
It uses a combination of natural language processing (NLP) and computer vision techniques to understand and process visual information. It’s trained on a large dataset of images and videos, which allows it to learn patterns and relationships between visual and textual data.
What are its strengths?
- State-of-the-art performance: It achieves state-of-the-art performance on a number of visual understanding benchmarks, including MathVista, DocVQA, and RealWorldQA.
- Fast and efficient: It is designed to be fast and efficient, making it suitable for a wide range of applications.
- Multimodal processing: It can process multiple types of input, including images, videos, and text.
Performance
Qwen2-VL-72B-Instruct-AWQ showcases remarkable performance in various tasks, especially in visual understanding and generation. Let’s dive into its speed, accuracy, and efficiency.
Speed
The model’s speed is impressive, with the ability to process large inputs efficiently. For example, when generating 2048 tokens with an input length of 1, the model achieves a speed of 8.90 tokens per second with BF16 quantization.
| Input Length | Quantization | GPU Num | Speed (tokens/s) | GPU Memory (GB) |
|---|---|---|---|---|
| 1 | BF16 | 2 | 8.90 | 138.74 |
| 6144 | BF16 | 2 | 6.53 | 148.66 |
| 14336 | BF16 | 3 | 4.39 | 165.92 |
Accuracy
The model achieves state-of-the-art performance on various visual understanding benchmarks, including:
- MathVista
- DocVQA
- RealWorldQA
- MTVQA
With an accuracy of 95.79% on DocVQA, the model demonstrates its ability to understand complex visual information.
| Benchmark | Accuracy |
|---|---|
| MathVista | 70.19% |
| DocVQA | 95.79% |
| MMBench | 86.94% |
| MathVista MINI | 70.19% |
Efficiency
The model’s efficiency is evident in its ability to process large inputs with minimal memory usage. For example, when generating 2048 tokens with an input length of 1, the model uses only 138.74 GB of GPU memory with BF16 quantization.
| Input Length | Quantization | GPU Num | GPU Memory (GB) |
|---|---|---|---|
| 1 | BF16 | 2 | 138.74 |
| 6144 | BF16 | 2 | 148.66 |
| 14336 | BF16 | 3 | 165.92 |
Limitations
While the model is incredibly powerful, it’s not perfect. It has limitations like:
- No Audio Support: It can’t understand audio information within videos.
- Data Timeliness: Its image dataset is only updated until June 2023, so it might not know about events or information after that date.
- Limited Capacity for Complex Instruction: It might struggle with intricate multi-step instructions.
- Insufficient Counting Accuracy: It’s not great at counting objects in complex scenes.
- Weak Spatial Reasoning Skills: It’s not perfect at understanding the relative positions of objects in 3D spaces.
Format
Qwen2-VL-72B-Instruct-AWQ is a multimodal model that can handle both images and videos, in addition to text. It uses a novel architecture that combines visual and textual information.
Architecture
The model is based on a transformer architecture, with a few key updates:
- Naive Dynamic Resolution: The model can handle images of arbitrary resolution, mapping them into a dynamic number of visual tokens.
- Multimodal Rotary Position Embedding (M-ROPE): This technique decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information.
Data Formats
The model supports the following data formats:
- Images: local files, base64, and URLs
- Videos: local files (currently)
- Text: tokenized text sequences
Special Requirements
- Input images can be resized to a range of resolutions, with a default range of 4-16384 visual tokens per image.
- The model can handle multiple images and videos as input.
- The model requires a specific pre-processing step for input data, using the
process_vision_infofunction.
Code Examples
Here are some code examples to get you started:
Single Image Inference
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# Load the model and processor
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-72B-Instruct-AWQ")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-72B-Instruct-AWQ")
# Prepare the input data
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preprocess the input data
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
# Run the model
generated_ids = model.generate(**inputs, max_new_tokens=128)
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(output_text)
Multi-Image Inference
# Messages containing multiple images and a text query
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/image1.jpg"},
{"type": "image", "image": "file:///path/to/image2.jpg"},
{"type": "text", "text": "Identify the similarities between these images."},
],
}
]
#... (rest of the code remains the same)
Video Inference
# Messages containing a video and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "file:///path/to/video1.mp4",
"max_pixels": 360 * 420,
"fps": 1.0,
},
{"type": "text", "text": "Describe this video."},
],
}
]
#... (rest of the code remains the same)
