InternVL2 5 2B
Meet InternVL2 5 2B, a cutting-edge multimodal large language model (MLLM) that's changing the game in AI. What makes it unique? For starters, it's built on a "ViT-MLP-LLM" paradigm, allowing it to handle complex vision-language tasks with ease. But that's not all - it's also been trained on a massive dataset, with a focus on high-quality multimodal instruction datasets. This means it can tackle tasks like multimodal reasoning, mathematics OCR, and chart understanding with impressive accuracy. But what about efficiency? InternVL2 5 2B has got that covered too, with a progressive scaling strategy that enables efficient training for complex vision-language tasks. And with a model size of just 2.21, it's surprisingly lightweight. So, what can you do with InternVL2 5 2B? From generating text to understanding images and videos, the possibilities are endless. Want to get started? Check out the quick start guide and start exploring the capabilities of this powerful model.
Table of Contents
Model Overview
The InternVL 2.5 model is a powerful multimodal large language model (MLLM) that combines computer vision and natural language processing. It’s designed to handle a wide range of tasks, from image and video understanding to text generation and multimodal reasoning.
Capabilities
The model is capable of handling various tasks, including:
- Multimodal reasoning
- Mathematics OCR
- Chart and document understanding
- Multi-image and real-world comprehension
- Visual grounding
- Multimodal multilingual understanding
Key Features
The model has several key features that make it stand out, including:
- Multimodal capabilities
- Dynamic high-resolution training
- Progressive scaling strategy
- Random JPEG compression
- Loss reweighting
- Data filtering pipeline
Training Data
The model is trained on a large-scale dataset that includes multimodal data, high-quality open-source data, and filtered data.
Training Strategy
The model is trained using a three-stage pipeline:
- MLP Warmup
- ViT Incremental Learning
- Full Model Instruction Tuning
Evaluation
The model has been evaluated on a range of multimodal tasks, including multimodal reasoning, mathematics OCR, chart and document understanding, and video understanding.
Performance
The model showcases remarkable performance, boasting impressive speed, accuracy, and efficiency in various tasks.
Limitations
While the model has made significant progress, it still has some limitations, including:
- Training data quality
- Multimodal understanding
- Language capability
- Robustness to adversarial attacks
- Dependence on compute resources
- Limited explainability
Format
The model accepts input in the form of text and images or videos, and uses a “ViT-MLP-LLM” paradigm to integrate a vision encoder with a language model.
Supported Data Formats
- Text: tokenized text sequences
- Images: 448x448 pixels, JPEG format
- Videos: 448x448 pixels, JPEG format, with each frame labeled with tags like “Frame-1”
Input Requirements
- Text input: must be tokenized and enclosed in
<text>and</text>tags - Image input: must be resized to 448x448 pixels and enclosed in
<img>and</img>tags - Video input: each frame must be resized to 448x448 pixels and labeled with tags like “Frame-1”, enclosed in
<img>and</img>tags
Output Requirements
- Output is generated in the form of text, with optional visualizations
Quick Start
To get started with the model, you can use the following example code:
import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL2_5-2B"
model = AutoModel.from_pretrained(path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda()
