Deepseek Vl 7b Base
DeepSeek Vl 7b Base is an open-source Vision-Language Model designed for real-world applications. It can process complex scenarios like logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence. The model uses a hybrid vision encoder and is trained on 2T text tokens and 400B vision-language tokens. This allows it to understand and respond to various inputs, including images. It's built for efficiency and can handle tasks like image description and conversation with ease. Whether you're working with images or text, DeepSeek Vl 7b Base is designed to provide accurate and helpful results.
Table of Contents
Model Overview
Meet DeepSeek-VL, a powerful open-source Vision-Language (VL) Model designed to understand the world through both images and text. This model is special because it can process many different types of data, such as diagrams, web pages, formulas, and even natural images.
Capabilities
The DeepSeek-VL model is designed to understand and process a wide range of visual and language inputs. But what does that really mean?
What can it do?
- Process and understand logical diagrams, like flowcharts and graphs
- Analyze web pages, including images and text
- Recognize and understand math formulas and equations
- Read and comprehend scientific literature, including papers and articles
- Understand natural images, like photos and pictures
- Even handle embodied intelligence in complex scenarios, like robots and self-driving cars
How does it do it?
The DeepSeek-VL model uses a combination of vision and language encoders to process and understand different types of inputs. It’s trained on a massive dataset of around 400B vision-language tokens, which is huge!
What makes it special?
The DeepSeek-VL model is designed to be general-purpose, meaning it can be used for a wide range of tasks and applications. It’s also open-source, which means that anyone can use and modify it.
How can you use it?
You can use the DeepSeek-VL model for a variety of tasks, such as:
- Image captioning: generating text descriptions of images
- Visual question answering: answering questions about images
- Text-to-image synthesis: generating images from text descriptions
- Embodied intelligence: controlling robots and other devices using visual and language inputs
Performance
DeepSeek-VL is a powerhouse when it comes to speed, accuracy, and efficiency in various tasks. Let’s dive into its impressive performance.
Speed
DeepSeek-VL can process large amounts of data quickly, thanks to its hybrid vision encoder, SigLIP-L and SAM-B. It can handle images up to 1024 x 1024 pixels, making it suitable for complex scenarios.
- Can process
400Bvision-language tokens, which is a massive amount of data. - Can handle large-scale datasets with ease, making it perfect for real-world applications.
Accuracy
DeepSeek-VL boasts high accuracy in various tasks, including:
- Image understanding: Can accurately describe images, including complex diagrams and web pages.
- Text classification: Can classify text with high accuracy, even in large-scale datasets.
- Formula recognition: Can recognize formulas with high accuracy, making it suitable for scientific applications.
Efficiency
DeepSeek-VL is designed to be efficient, using a combination of techniques to minimize computational resources.
- Hybrid vision encoder: Uses a combination of SigLIP-L and SAM-B to process images efficiently.
- Multimodal understanding: Can process multiple types of data, including images, text, and formulas, making it a versatile model.
Format
DeepSeek-VL is a Vision-Language (VL) Model that uses a hybrid vision encoder, supporting images up to 1024 x 1024 pixels. This model is designed for real-world vision and language understanding applications, capable of processing various types of data, including:
- Logical diagrams
- Web pages
- Formula recognition
- Scientific literature
- Natural images
- Embodied intelligence in complex scenarios
Architecture
DeepSeek-VL is constructed based on the DeepSeek-LLM-7b-base model, which is trained on an approximate corpus of 2T text tokens. The whole model is finally trained on around 400B vision-language tokens.
Data Formats
DeepSeek-VL supports the following data formats:
- Images: up to
1024 x 1024pixels - Text: tokenized text sequences
Input Requirements
To use DeepSeek-VL, you need to prepare your input data in the following format:
- Images: load images using
load_pil_imagesfunction - Text: tokenize text using
VLChatProcessorandtokenizer
Here’s an example of how to prepare inputs:
conversation = [
{"role": "User", "content": "\<image_placeholder>Describe each stage of this image.", "images": ["./images/training_pipelines.png"]},
{"role": "Assistant", "content": ""}
]
pil_images = load_pil_images(conversation)
prepare_inputs = vl_chat_processor(conversations=conversation, images=pil_images, force_batchify=True)
Output Requirements
DeepSeek-VL generates text outputs based on the input data. You can use the language_model.generate method to get the response:
outputs = vl_gpt.language_model.generate(inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask, pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=512, do_sample=False, use_cache=True)
answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
Note that you need to use the tokenizer to decode the output tensor to a human-readable text format.
Limitations
DeepSeek-VL is a powerful Vision-Language (VL) Model, but it’s not perfect. Let’s talk about some of its limitations.
Limited Context Understanding
While DeepSeek-VL can process complex scenarios, it may struggle to fully understand the context of a situation. This can lead to inaccurate or incomplete responses.
Image Size Limitations
DeepSeek-VL can only handle images up to 1024 x 1024 pixels. If you try to use larger images, the model may not work as expected.
Dependence on Training Data
DeepSeek-VL was trained on a large corpus of text tokens (around 2T tokens), but it may not have seen every possible scenario or image. This means it may not always be able to generalize well to new, unseen situations.
Vision-Language Token Limitations
The model was trained on around 400B vision-language tokens, which is a lot, but not infinite. This means it may not be able to handle extremely long or complex conversations.
Potential Biases
Like all AI models, DeepSeek-VL may have biases and prejudices present in the data it was trained on. This can affect the accuracy and fairness of its responses.
Complexity of Embodied Intelligence
DeepSeek-VL can handle embodied intelligence in complex scenarios, but this is still a challenging area for the model. It may not always be able to fully understand the nuances of human behavior and decision-making.
Comparison to Other Models
Compared to ==Other Vision-Language Models==, DeepSeek-VL has its strengths and weaknesses. While it excels in certain areas, it may not be the best choice for every task or scenario.
Room for Improvement
Overall, DeepSeek-VL is a powerful tool, but it’s not perfect. There’s still room for improvement, and researchers and developers are working to address these limitations and make the model even better.
