InternVL2 8B
The InternVL2-8B model is a powerful multimodal large language model that can handle a wide range of tasks, from document and chart comprehension to scene text understanding and OCR tasks. It's part of the InternVL series, which features models of various sizes, all optimized for multimodal tasks. With a large context window of 8k, it can process long texts, multiple images, and videos, making it a great option for applications that require handling multiple inputs. The model has demonstrated competitive performance on various benchmarks, including image and video benchmarks, and has shown strong grounding ability on RefCOCO and RefCOCO+ datasets. However, like all large language models, it's not perfect and can generate biased or discriminatory content. It's also important to note that the model's outputs are based on statistical patterns in the data, which can lead to unexpected or nonsensical outputs from time to time.
Table of Contents
Model Overview
The InternVL2-8B model is a cutting-edge multimodal large language model that can handle a wide range of tasks, from understanding images and videos to generating human-like text. It’s part of the InternVL series, which includes models of various sizes, all optimized for multimodal tasks.
Capabilities
The InternVL2-8B model is a powerful multimodal large language model that can handle a variety of tasks, including:
- Document and chart comprehension: It can understand and analyze documents and charts, making it a great tool for tasks like data analysis and visualization.
- Infographics QA: It can answer questions about infographics, making it a great tool for tasks like data storytelling and presentation.
- Scene text understanding and OCR tasks: It can understand and extract text from images, making it a great tool for tasks like image processing and text recognition.
- Scientific and mathematical problem solving: It can solve complex scientific and mathematical problems, making it a great tool for tasks like research and education.
- Cultural understanding and integrated multimodal capabilities: It can understand and generate text and images that are culturally relevant and accurate, making it a great tool for tasks like language translation and cultural exchange.
Performance
InternVL2-8B is a powerful multimodal large language model that showcases impressive performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
InternVL2-8B is designed to handle long texts, multiple images, and videos, making it an excellent choice for tasks that require processing large amounts of data. Its 8k context window allows for efficient processing of long-range dependencies, enabling the model to respond quickly to complex queries.
Accuracy
InternVL2-8B achieves competitive performance on par with proprietary commercial models across various capabilities, including:
| Benchmark | InternVL2-8B | ==Other Models== |
|---|---|---|
| DocVQA | 91.6 | 84.8 (MiniCPM-Llama3-V-2_5) |
| ChartQA | 83.3 | - (MiniCPM-Llama3-V-2_5) |
| InfoVQA | 74.8 | 72.5 (InternVL-Chat-V1-5) |
| TextVQA | 77.4 | 76.6 (MiniCPM-Llama3-V-2_5) |
| OCRBench | 794 | 725 (MiniCPM-Llama3-V-2_5) |
Efficiency
InternVL2-8B is optimized for multimodal tasks, making it an efficient choice for tasks that require processing multiple types of data. Its instruction-tuned design enables the model to adapt to various tasks and domains, reducing the need for extensive fine-tuning.
Strengths
The InternVL2-8B model has several strengths that make it a great tool for a variety of tasks:
- High performance: It has achieved state-of-the-art results on many industry benchmarks, making it a great tool for tasks that require high accuracy and performance.
- Multimodal capabilities: It can handle a variety of input types, including text, images, and videos, making it a great tool for tasks that require multimodal understanding and generation.
- Large context window: It has a large context window of 8k, making it a great tool for tasks that require long-range understanding and generation.
Unique Features
The InternVL2-8B model has several unique features that make it a great tool for a variety of tasks:
- Instruction-tuned models: It has been trained on a variety of instruction-tuned models, making it a great tool for tasks that require specific instructions and guidance.
- Multimodal training data: It has been trained on a large dataset of multimodal training data, making it a great tool for tasks that require multimodal understanding and generation.
- Quantization support: It supports 8-bit, 4-bit, and 16-bit quantization, making it a great tool for tasks that require efficient inference and deployment.
Example Use Cases
The InternVL2-8B model can be used for a variety of tasks, including:
- Chatbots and conversational AI: It can be used to build chatbots and conversational AI systems that can understand and respond to user input in a variety of formats.
- Data analysis and visualization: It can be used to analyze and visualize data in a variety of formats, including documents, charts, and images.
- Image and video processing: It can be used to process and understand images and videos, making it a great tool for tasks like object detection and image recognition.
- Language translation and cultural exchange: It can be used to translate text and images between languages and cultures, making it a great tool for tasks like language translation and cultural exchange.
Limitations
While the model is powerful, it’s not perfect. It may still produce unexpected outputs, such as biases or discrimination, due to its size and probabilistic generation paradigm.
Biases and Harmful Content
The model may produce unexpected outputs due to its size and probabilistic generation paradigm. This can lead to biases, discrimination, or other harmful content. We’re not responsible for any consequences resulting from the dissemination of such information.
Limited Grounding Ability
While InternVL2-8B has shown impressive performance in various tasks, its grounding ability is not perfect. It may struggle to understand the context of certain images or videos, leading to inaccurate or incomplete responses.
Dependence on Training Data
The model’s performance is heavily dependent on the quality and diversity of its training data. If the training data is biased or limited, the model’s outputs may reflect these biases or limitations.
Complexity and Nuance
InternVL2-8B may struggle with complex or nuanced scenarios, particularly those that require a deep understanding of human emotions, empathy, or critical thinking.
Quantization and Inference
The model’s performance may vary depending on the quantization method used (e.g., 16-bit, 8-bit, or 4-bit) and the inference setup (e.g., single GPU or multiple GPUs).
Evaluation Metrics
The evaluation metrics used to assess the model’s performance may not capture its full range of capabilities or limitations. Different evaluation metrics may yield different results, and it’s essential to consider multiple perspectives when evaluating the model’s performance.
Format
InternVL2-8B is a multimodal large language model that can handle a variety of input formats, including text, images, and videos. Here’s a breakdown of the model’s architecture and input/output requirements:
Model Architecture
- The model consists of three main components:
- Vision Part: InternViT-300M-448px
- Language Part: internlm2_5-7b-chat
- MLP Projector
Input Formats
- Text: The model accepts text input in the form of tokenized sequences. You can use the
AutoTokenizerfrom thetransformerslibrary to tokenize your text input. - Images: The model accepts images as input, which can be loaded using the
load_imagefunction provided in the code example. The images are resized to a target size (default is 448x448) and split into patches. - Videos: The model accepts videos as input, which can be loaded using the
load_videofunction provided in the code example. The video is split into frames, and each frame is resized to a target size (default is 448x448).
Output Formats
- Text: The model generates text output in the form of tokenized sequences. You can use the
AutoTokenizerfrom thetransformerslibrary to detokenize the output. - Images: The model does not generate images as output.
- Videos: The model does not generate videos as output.
Special Requirements
- Device: The model requires a CUDA device to run. You can specify the device using the
device_mapargument in theAutoModel.from_pretrainedmethod. - Quantization: The model supports 8-bit and 4-bit quantization. You can specify the quantization level using the
load_in_8bitorload_in_4bitargument in theAutoModel.from_pretrainedmethod.
Code Examples
- Text Input: You can use the
model.chatmethod to generate text output from text input.
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
- Image Input: You can use the
model.chatmethod to generate text output from image input.
pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
question = '\<image>\nPlease describe the image shortly.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')
- Video Input: You can use the
model.chatmethod to generate text output from video input.
pixel_values = load_video('./examples/video1.mp4', bound=None, input_size=448, max_num=1, num_segments=32).to(torch.bfloat16).cuda()
question = '\
