Welcome to the comprehensive guide on using the InternVL2-26B model, a robust multimodal large language model that goes beyond basic functionalities. Whether you want to understand its performance or integrate it into your projects, we’ve got you covered!

Getting Started with InternVL2-26B

To begin using InternVL2-26B, follow these straightforward steps:

Setup and Installation

• Ensure you have Python and pip installed on your machine.
• Run the following command to install the required dependencies:

pip install transformers lmdeploy decord torchvision

• Also, it’s crucial to use transformers version 4.37.2. You can specify the version like so:

pip install transformers==4.37.2

Loading the Model

Follow these steps to load the model:

• Use the following code snippet to load the model in either 16-bit or 8-bit quantization:

import torch
from transformers import AutoModel, AutoTokenizer

model_name = "OpenGVLab/InternVL2-26B"
model = AutoModel.from_pretrained(model_name, torch_dtype=torch.bfloat16).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)

Inference with InternVL2-26B

To interact with the model, use the following methods:

Text Conversations

• For a simple text conversation:

question = "Hello, who are you?"
response, _ = model.chat(tokenizer, None, question)
print(f'User: {question}\nAssistant: {response}')

Image Interaction

• Load and interact with an image:

image_file = "./examples/image1.jpg"
pixel_values = load_image(image_file, max_num=12).to(torch.bfloat16).cuda()
question = "\nPlease describe the image."
response = model.chat(tokenizer, pixel_values, question)
print(f'User: {question}\nAssistant: {response}')

Video Processing

• Load a video and ask questions about it:

video_path = "./examples/video.mp4"
pixel_values, _ = load_video(video_path)
question = "What is happening in the video?"
response = model.chat(tokenizer, pixel_values, question)
print(f'User: {question}\nAssistant: {response}')

Performance Insights

InternVL2-26B comes with impressive benchmark scores compared to its predecessors. It effectively handles multimodal tasks such as document and chart comprehension, while also being significantly enhanced with a larger context window and diverse training data. This versatility is akin to a Swiss army knife, seamlessly transitioning between different tools based on the user’s needs.

Troubleshooting Common Issues

Here are some common issues you might encounter and how to resolve them:

• Error while loading models: Check internet connection and ensure transformers library version is correctly set.
• Performance lag: Make sure your hardware meets the model requirements; ideally, use a GPU.
• Unexpected Outputs: The model might produce biased or nonsensical responses due to its probabilistic nature. Always validate outputs for accuracy.
• If issues persist, explore for insights or collaboration at fxis.ai.

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

Conclusion

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.

Now you’re equipped to utilize the power of InternVL2-26B. Dive in and explore the depths of multimodal capabilities!