A GPT-4V Level Multimodal LLM on Your Phone

Imagine having the capability to understand images and texts in a multilingual context right in your pocket. That’s what MiniCPM-Llama3-V 2.5, our latest model, offers. With advanced features surpassing even the popular GPT-4V, it empowers users to engage in rich multimodal interactions seamlessly across devices.

Getting Started with MiniCPM-Llama3-V 2.5

To utilize MiniCPM-Llama3-V 2.5, you need to install a few requirements and run it using Python. Here’s a straightforward way to set up the environment:

• Make sure Python 3.10 or above is installed.
• Install necessary libraries by executing the following command:

pip install Pillow==10.1.0 torch==2.1.2 torchvision==0.16.2 transformers==4.40.0 sentencepiece==0.1.99

• Prepare your script to call the model.

Sample Code for Inference

Let’s break down the code you’ll need to run MiniCPM-Llama3-V 2.5. Think of it as a recipe for baking a delicious cake:

• You gather your ingredients (packages) – in this case, the libraries.
• Next, you prep your workspace by pulling in the model – much like preheating your oven.
• Finally, you mix everything together in a specific order to get your perfect outcome (output generated from the model).

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

# Load the model
model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16)
model.eval()

# Prepare the image and question
image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': question}]

# Get the result
res = model.chat(image=image, msgs=msgs, tokenizer=tokenizer, sampling=True, temperature=0.7)

# Displaying output
for new_text in res:
    print(new_text, flush=True, end='')  

Key Features

• Leading Performance: Achieves an average score of 65.1 on OpenCompass, outperforming other proprietary models.
• Strong OCR Capabilities: Processes images with impressive accuracy, exceeding models like GPT-4o.
• Multilingual Support: Facilitates communication in over 30 languages, unlocking a world of possibilities.
• Efficient Deployment: Optimized for mobile devices, fostering a smooth user experience.

Troubleshooting Tips

In the event you encounter any issues, consider the following troubleshooting steps:

• Ensure that all required Python libraries are installed correctly.
• Verify that your system meets the performance specifications for running the model, particularly with regard to available GPU memory.
• If you experience errors in loading the model, check your internet connection or try downloading the model manually from the specified URL.
• For compatibility issues, ensure that you are using Python 3.10.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

MiniCPM-Llama3-V 2.5 introduces exciting advancements in AI, enabling us to bridge visual and textual understanding like never before. It’s an exciting prospect for both developers and end-users alike.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.