MiniCPM V 2 6 Rk3588 1.1.4
The MiniCPM-V 2.6 model is a powerful tool for image and video understanding, capable of processing multiple images and videos, and even performing in-context learning. But what makes it truly remarkable is its efficiency - it can run on end-side devices like iPads and achieve state-of-the-art performance with a relatively small size. How does it do this? By using techniques like token density and LoRA, it's able to produce fewer tokens while maintaining accuracy, resulting in faster inference speeds and lower memory usage. This model is not just for research - it's designed to be used in real-world applications, and its ease of use and flexibility make it a great choice for a wide range of tasks.
Table of Contents
Model Overview
Meet the MiniCPM-V 2.6 model, a cutting-edge AI designed for various tasks like image and video understanding, conversation, and reasoning. This model is built on top of SigLip-400M and Qwen2-7B, boasting a total of 8B parameters.
Capabilities
The MiniCPM-V 2.6 model is a powerful multimodal language model that can process and understand images, videos, and text. It’s designed to be efficient and can run on devices like iPads.
- Single Image Understanding: It can look at an image and answer questions about it.
- Multi Image Understanding: It can compare and contrast multiple images and answer questions about them.
- Video Understanding: It can watch a video and describe what’s happening in it.
- In-context Learning: It can learn from a few examples and apply that knowledge to new situations.
- OCR Capability: It can read text from images and answer questions about it.
Performance
MiniCPM-V 2.6 is a powerhouse when it comes to performance. Let’s dive into its impressive capabilities.
Speed
How fast can a model process images and videos? MiniCPM-V 2.6 can handle images with up to 1.8M pixels and produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models. This results in faster inference speed, lower latency, and reduced memory usage.
Accuracy
MiniCPM-V 2.6 achieves state-of-the-art performance in various tasks, including:
- Single image understanding: outperforms widely used proprietary models like ==GPT-4o mini==, ==GPT-4V==, and ==Gemini 1.5 Pro==
- Multi-image understanding: achieves state-of-the-art performance on popular benchmarks like Mantis-Eval and BLINK
- Video understanding: outperforms ==GPT-4V== and ==Claude 3.5 Sonnet== on Video-MME
- OCR capability: achieves state-of-the-art performance on OCRBench, surpassing proprietary models like GPT-4o and ==Gemini 1.5 Pro==
How to Use
MiniCPM-V 2.6 is designed to be easy to use, with various ways to integrate it into your projects.
- Easy Usage: You can use it with popular libraries like Hugging Face Transformers and llama.cpp.
- Chat with Images and Videos: You can ask it questions about images and videos, and it will respond with answers.
- In-context Few-shot Learning: You can teach it new things with just a few examples, and it will apply that knowledge to new situations.
Code Examples
Here are some code examples to get you started:
- Single Image Input:
image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': [image, question]}]
res = model.chat(image=None, msgs=msgs, tokenizer=tokenizer)
print(res)
- Multiple Images Input:
image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
msgs = [{'role': 'user', 'content': [image1, image2, question]}]
answer = model.chat(image=None, msgs=msgs, tokenizer=tokenizer)
print(answer)
- Video Input:
video_path = "video_test.mp4"
frames = encode_video(video_path)
question = "Describe the video"
msgs = [{'role': 'user', 'content': frames + [question]}]
answer = model.chat(image=None, msgs=msgs, tokenizer=tokenizer, **params)
print(answer)
Note: The encode_video function is not shown here, but it can be found in the original code snippet.
Limitations
While MiniCPM-V 2.6 is a powerful tool, it’s not perfect. Let’s take a closer look at some of its limitations.
- Limited Understanding of Context: While MiniCPM-V 2.6 can process multiple images and videos, it may struggle to fully understand the context of the input.
- Dependence on Training Data: MiniCPM-V 2.6 is only as good as the data it was trained on. If the training data is biased or incomplete, the model’s performance may suffer.
- Hallucination Risks: Like other AI models, MiniCPM-V 2.6 can generate responses that are not based on actual facts.
