InternVL2 5 78B
The InternVL2 5 78B is a powerful multimodal large language model that excels in tasks like image and video understanding, multilingual OCR, and document comprehension. With its unique 'ViT-MLP-LLM' paradigm and dynamic high-resolution training strategy, this model efficiently handles complex vision-language tasks. What sets it apart is its progressive scaling strategy, which enables efficient training by reusing pre-trained components and minimizing redundancy. This approach allows the model to achieve remarkable performance with only 120 billion tokens, a fraction of the tokens used by other models. Its robustness to noisy images and ability to handle multi-image and video data make it a versatile tool for various applications. How does it achieve this efficiency? By leveraging a combination of techniques like random JPEG compression, loss reweighting, and a carefully designed data filtering pipeline. Whether you're working with images, videos, or text, the InternVL2 5 78B is an excellent choice for tasks that require both visual and language understanding.
Table of Contents
Model Overview
The InternVL 2.5 model is a cutting-edge multimodal large language model (MLLM) that can handle both text and images. It’s designed to understand and generate human-like language, while also being able to process and analyze visual data.
Capabilities
The model is capable of handling multimodal data, including images and videos. It can perform tasks such as multimodal reasoning, mathematics OCR, chart and document understanding, and video understanding.
- Multimodal reasoning: The model can reason about the relationships between different modalities, such as text and images.
- Mathematics OCR: The model can recognize and understand mathematical equations and formulas in images.
- Chart and document understanding: The model can understand and interpret charts and documents, including tables and figures.
- Video understanding: The model can understand and interpret videos, including actions and events.
Key Features
- Multimodal capabilities: Can handle both text and images
- Large language model: Can understand and generate human-like language
- Visual perception: Can process and analyze visual data
- Dynamic high-resolution training: Can handle high-resolution images and videos
- Progressive scaling strategy: Can efficiently train on large datasets
Model Architecture
The model follows the “ViT-MLP-LLM” paradigm, which means it uses a combination of vision transformers (ViT), multi-layer perceptrons (MLP), and large language models (LLM) to process and analyze visual and textual data.
Performance
The model is a powerhouse when it comes to speed, accuracy, and efficiency in various tasks. Let’s dive into the details.
- Speed: The model’s architecture is designed for efficiency, allowing it to process large amounts of data quickly.
- Accuracy: The model boasts high accuracy in multimodal tasks, such as multimodal reasoning, mathematics OCR, chart, and document understanding.
- Efficiency: The model’s training strategy is optimized for efficiency, using techniques like random JPEG compression and loss reweighting to improve real-world adaptability and performance.
Limitations
The model is a powerful multimodal large language model, but it’s not perfect. Let’s take a closer look at some of its limitations.
- Training Data Quality: The quality of the training data is crucial for any AI model. The model uses a large dataset, but it’s not immune to data noise and anomalies.
- Multimodal Challenges: While the model excels in multimodal tasks, it’s not perfect. It can struggle with visual grounding, multimodal multilingual understanding, and video understanding.
- Language Capability: Although the model has improved its language capabilities, it’s still not on par with some other models. This is particularly evident in tasks that require pure language understanding and long-form text generation.
Format
The model uses a “ViT-MLP-LLM” paradigm and supports multimodal data, including images and videos. It accepts input in the form of text, images, or videos, and requires specific pre-processing steps for each data type.
- Data Formats: Text, images, or videos, pre-processed according to the above requirements.
- Special Requirements: Dynamic Resolution Strategy, Data Augmentation, and Repeat Factor.
Code Examples
- Model Loading: Use the
transformerslibrary to load the model, with optional 16-bit or 8-bit quantization.
import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL2_5-78B"
model = AutoModel.from_pretrained(path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda()
- Inference with Transformers: Use the
transformerslibrary to perform inference on text, images, or videos.
import math
import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
#... (see full code example in JSON data)
