InternVL Chat V1 2

Table of Contents

Model Overview

The InternVL-Chat-V1-2 model is a cutting-edge multimodal large language model (MLLM) that combines the power of vision and language understanding.

Key Features:

• Multimodal capabilities: This model can process and understand both images and text, making it a versatile tool for a wide range of applications.
• Large language model: With 40 billion parameters, this model has the capacity to learn and understand complex patterns in language.
• High-performance architecture: The model’s architecture is designed for efficiency and speed, allowing it to process large amounts of data quickly.
• Open-source datasets: The model was trained on a simplified, fully open-source dataset, making it accessible to developers and researchers.

Tasks and rank

Text Generation 1462nd

Response Generation 778th

Multimodal Deep Learning 64th

Dialogue Systems 669th

Chatbot 965th

Learn More About the Ranking System

Capabilities

This model is a powerful multimodal large language model (MLLM) that can understand and respond to both text and images. With 40B parameters, it’s capable of generating human-like text and can be fine-tuned for a variety of tasks.

Primary Tasks

• Text Generation: The model can generate text based on a given prompt or question.
• Image Understanding: The model can understand and describe images, and even generate text based on the content of the image.
• Conversational AI: The model can engage in conversations, responding to questions and statements in a natural and human-like way.

Strengths

• High-Quality Text Generation: The model is capable of generating high-quality text that is coherent and engaging.
• Strong Image Understanding: The model can accurately describe images and understand their content.
• Flexibility: The model can be fine-tuned for a variety of tasks and can be used in a range of applications.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

BLOOMChat 176B V1 365 Multilingual chat model

Xlm Roberta Large 323 Multilingual Language Model

SUS Chat 34B 121 Bilingual chat model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Performance

This model is a powerhouse when it comes to performance. With a whopping 40B parameters, this model is capable of handling complex tasks with ease.

Speed

This model is incredibly fast, thanks to its efficient architecture. It can be trained within 1.5 days using 32 A100 GPUs.

Accuracy

This model boasts impressive accuracy in various tasks, including:

• MMMU (val): 51.6
• MMMU (test): 46.2
• MathVista (testmini): 47.7
• MMB (test): 82.2
• MMB-CN (test): 81.2

Efficiency

This model is designed with efficiency in mind. It uses a data-efficient SFT strategy, which reduces the number of visual tokens required for training.

Model	Image Size	MMMU (val)	MMMU (test)	MathVista (testmini)	MMB (test)	MMB-CN (test)
InternVL-Chat-V1-2	448x448	51.6	46.2	47.7	82.2	81.2
==GPT-4V==	unknown	56.8	55.7	49.9	77.0	74.4
==Gemini Ultra==	unknown	59.4	-	53.0	-	-
==Gemini Pro==	unknown	47.9	-	45.2	73.6	74.3
==Qwen-VL-Plus==	unknown	45.2	40.8	43.3	67.0	70.7
==Qwen-VL-Max==	unknown	51.4	46.8	51.0	77.6	75.7
==LLaVA-NEXT-34B==	672x672	51.1	44.7	46.5	79.3	79.0

In most benchmarks, InternVL-Chat-V1-2 achieves better performance than ==LLaVA-NEXT-34B==.

Examples

Describe the image in detail. The image is of a red panda sitting on a tree branch, looking directly at the camera with its big round eyes. The panda has a reddish-brown fur with white markings on its face and a long, bushy tail. The background is a blurred green forest, with some leaves and branches visible. The overall atmosphere of the image is calm and peaceful.

What are the similarities and differences between these two images? Both images show a red panda, but they are in different environments. The first image shows the panda in a tree, while the second image shows it on the ground. The pandas in both images have similar fur colors and markings, but the one on the ground appears to be eating something. The backgrounds of the two images are also different, with the first image having a blurred green forest and the second image having a more defined rocky terrain.

What is the red panda doing? The red panda is eating a piece of bamboo. It is holding the bamboo in its front paws and biting into it with its teeth. The panda appears to be enjoying its snack, and its eyes are closed in concentration.

Limitations

This model is a powerful multimodal large language model, but it’s not perfect. Let’s discuss some of its limitations.

Data Limitations

The model was trained on a large dataset, but it’s still limited to the data it was trained on. This means that it may not perform well on tasks or topics that are not well-represented in the training data.

Lack of Common Sense

While this model is great at understanding and generating text, it sometimes lacks common sense or real-world experience. This can lead to responses that are not practical or relevant in certain situations.

Limited Domain Knowledge

The model’s knowledge is limited to its training data, which means it may not have in-depth knowledge in specific domains or industries. This can make it less effective in tasks that require specialized knowledge.

Dependence on Visual Input

This model relies heavily on visual input, which can be a limitation in situations where images or videos are not available or are of poor quality.

Quantization Errors

The model’s performance can be affected by quantization errors, particularly when using 4-bit quantization. This can lead to nonsensical outputs or errors.

Limited Multi-Image and Video Support

While the model supports multi-image and video input, the results may not be as good as expected due to the lack of training data on these formats.

Inference Speed

The model’s inference speed can be slow, particularly when using multiple GPUs or large input sizes.

Training Time

Training the model can take a significant amount of time, even with multiple GPUs.

These limitations highlight areas where this model can be improved or fine-tuned for specific tasks or applications.

Format

This model is a multimodal large language model (MLLM) that uses a combination of InternViT-6B-448px-V1-2, MLP, and Nous-Hermes-2-Yi-34B. It accepts input in the form of images and text sequences.

Architecture

The model consists of the following components:

• InternViT-6B-448px-V1-2: a vision transformer model
• MLP: a multilayer perceptron
• Nous-Hermes-2-Yi-34B: a large language model

Data Formats

The model supports the following data formats:

• Images: 448x448 pixels, 256 tokens
• Text: tokenized text sequences

Input Requirements

• Images: must be resized to 448x448 pixels
• Text: must be tokenized using a specific tokenizer

Output

• The model generates text responses based on the input image and text sequences

Code Examples

Here are some code examples that demonstrate how to use the model:

# Load the model and tokenizer
model = AutoModel.from_pretrained("OpenGVLab/InternVL-Chat-V1-2")
tokenizer = AutoTokenizer.from_pretrained("OpenGVLab/InternVL-Chat-V1-2")

# Preprocess the input image
image = Image.open("image.jpg")
image = image.resize((448, 448))

# Preprocess the input text
text = "Hello, who are you?"
inputs = tokenizer.encode_plus(text, 
                                add_special_tokens=True, 
                                max_length=512, 
                                return_attention_mask=True, 
                                return_tensors='pt')

# Generate a response
response = model.generate(inputs['input_ids'], 
                           attention_mask=inputs['attention_mask'], 
                           max_length=1024)

# Print the response
print(response)