deepseek-vl-7b-chat

Maintainer: deepseek-ai

Total Score

184

Last updated 5/17/2024

🌐

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

deepseek-vl-7b-chat is an instructed version of the deepseek-vl-7b-base model, which is an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. The deepseek-vl-7b-base model uses the SigLIP-L and SAM-B as the hybrid vision encoder, and is constructed based on the deepseek-llm-7b-base model, which is trained on an approximate corpus of 2T text tokens. The whole deepseek-vl-7b-base model is finally trained around 400B vision-language tokens.

The deepseek-vl-7b-chat model is an instructed version of the deepseek-vl-7b-base model, making it capable of engaging in real-world vision and language understanding applications, including processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios.

Model inputs and outputs

Inputs

  • Image: The model can take images as input, supporting a resolution of up to 1024 x 1024.
  • Text: The model can also take text as input, allowing for multimodal understanding and interaction.

Outputs

  • Text: The model can generate relevant and coherent text responses based on the provided image and/or text inputs.
  • Bounding Boxes: The model can also output bounding boxes, enabling it to localize and identify objects or regions of interest within the input image.

Capabilities

deepseek-vl-7b-chat has impressive capabilities in tasks such as visual question answering, image captioning, and multimodal understanding. For example, the model can accurately describe the content of an image, answer questions about it, and even draw bounding boxes around relevant objects or regions.

What can I use it for?

The deepseek-vl-7b-chat model can be utilized in a variety of real-world applications that require vision and language understanding, such as:

  • Content Moderation: The model can be used to analyze images and text for inappropriate or harmful content.
  • Visual Assistance: The model can help visually impaired users by describing images and answering questions about their contents.
  • Multimodal Search: The model can be used to develop search engines that can understand and retrieve relevant information from both text and visual sources.
  • Education and Training: The model can be used to create interactive educational materials that combine text and visuals to enhance learning.

Things to try

One interesting thing to try with deepseek-vl-7b-chat is its ability to engage in multi-round conversations about images. By providing the model with an image and a series of follow-up questions or prompts, you can explore its understanding of the visual content and its ability to reason about it over time. This can be particularly useful for tasks like visual task planning, where the model needs to comprehend the scene and take multiple steps to achieve a goal.

Another interesting aspect to explore is the model's performance on specialized tasks like formula recognition or scientific literature understanding. By providing it with relevant inputs, you can assess its capabilities in these domains and see how it compares to more specialized models.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

⛏️

deepseek-llm-7b-chat

deepseek-ai

Total Score

65

deepseek-llm-7b-chat is a 7 billion parameter language model developed by DeepSeek AI. It has been trained from scratch on a vast 2 trillion token dataset, with 87% code and 13% natural language in both English and Chinese. DeepSeek AI also offers larger model sizes up to 67 billion parameters with the deepseek-llm-67b-chat model, as well as a series of code-focused models under the deepseek-coder line. The deepseek-llm-7b-chat model has been fine-tuned on extra instruction data, allowing it to engage in natural language conversations. This contrasts with the base deepseek-llm-7b-base model, which is focused more on general language understanding. The deepseek-vl-7b-chat takes the language model a step further by incorporating vision-language capabilities, enabling it to understand and reason about visual content as well. Model inputs and outputs Inputs Text**: The model accepts natural language text as input, which can include prompts, conversations, or other types of text-based communication. Images**: Some DeepSeek models, like deepseek-vl-7b-chat, can also accept image inputs to enable multimodal understanding and generation. Outputs Text Generation**: The primary output of the model is generated text, which can range from short responses to longer form content. The model is able to continue a conversation, answer questions, or generate original text. Code Generation**: For the deepseek-coder models, the output includes generated code snippets and programs in a variety of programming languages. Capabilities The deepseek-llm-7b-chat model demonstrates strong natural language understanding and generation capabilities. It can engage in open-ended conversations, answering questions, providing explanations, and even generating creative content. The model's large training dataset and fine-tuning on instructional data gives it a broad knowledge base and the ability to follow complex prompts. For users looking for more specialized capabilities, the deepseek-vl-7b-chat and deepseek-coder models offer additional functionality. The deepseek-vl-7b-chat can process and reason about visual information, making it well-suited for tasks involving diagrams, images, and other multimodal content. The deepseek-coder series focuses on code-related abilities, demonstrating state-of-the-art performance on programming tasks and benchmarks. What can I use it for? The deepseek-llm-7b-chat model can be a versatile tool for a wide range of applications. Some potential use cases include: Conversational AI**: Develop chatbots, virtual assistants, or dialogue systems that can engage in natural, contextual conversations. Content Generation**: Create original text content such as articles, stories, or scripts. Question Answering**: Build applications that can provide informative and insightful answers to user questions. Summarization**: Condense long-form text into concise, high-level summaries. For users with more specialized needs, the deepseek-vl-7b-chat and deepseek-coder models open up additional possibilities: Multimodal Reasoning**: Develop applications that can understand and reason about the relationships between text and visual information, like diagrams or technical documentation. Code Generation and Assistance**: Build tools that can generate, explain, or assist with coding tasks across a variety of programming languages. Things to try One interesting aspect of the deepseek-llm-7b-chat model is its ability to engage in open-ended, multi-turn conversations. Try providing the model with a prompt that sets up a scenario or persona, and see how it responds and builds upon the dialogue. You can also experiment with giving the model specific instructions or tasks to test its adaptability and problem-solving skills. For users interested in the multimodal capabilities of the deepseek-vl-7b-chat model, try providing the model with a mix of text and images to see how it interprets and reasons about the combined information. This could involve describing an image and having the model generate a response, or asking the model to explain the content of a technical diagram. Finally, the deepseek-coder models offer a unique opportunity to explore the intersection of language and code. Try prompting the model with a partially complete code snippet and see if it can fill in the missing pieces, or ask it to explain the functionality of a given piece of code.

Read more

Updated Invalid Date

AI model preview image

deepseek-vl-7b-base

lucataco

Total Score

3

DeepSeek-VL is an open-source Vision-Language (VL) model designed for real-world vision and language understanding applications. Developed by the team at DeepSeek AI, the model possesses general multimodal understanding capabilities, allowing it to process logical diagrams, web pages, formula recognition, scientific literature, natural images, and even embodied intelligence in complex scenarios. Similar models include moondream2, a small vision language model designed for edge devices, llava-13b, a large language and vision model with GPT-4 level capabilities, and phi-3-mini-4k-instruct, a lightweight, state-of-the-art open model trained with the Phi-3 datasets. Model inputs and outputs The DeepSeek-VL model accepts a variety of inputs, including images, text prompts, and conversations. It can generate responses that combine visual and language understanding, making it suitable for a wide range of applications. Inputs Image**: An image URL or file that the model will analyze and incorporate into its response. Prompt**: A text prompt that provides context or instructions for the model to follow. Max New Tokens**: The maximum number of new tokens the model should generate in its response. Outputs Response**: A generated response that combines the model's visual and language understanding to address the provided input. Capabilities The DeepSeek-VL model excels at tasks that require multimodal reasoning, such as image captioning, visual question answering, and document understanding. It can analyze complex scenes, recognize logical diagrams, and extract information from scientific literature. The model's versatility makes it suitable for a variety of real-world applications. What can I use it for? DeepSeek-VL can be used for a wide range of applications that require vision-language understanding, such as: Visual question answering**: Answering questions about the content and context of an image. Image captioning**: Generating detailed descriptions of images. Multimodal document understanding**: Extracting information from documents that combine text and images, such as scientific papers or technical manuals. Logical diagram understanding**: Analyzing and understanding the content and structure of logical diagrams, such as those used in engineering or mathematics. Things to try Experiment with the DeepSeek-VL model by providing it with a diverse range of inputs, such as images of different scenes, diagrams, or scientific documents. Observe how the model combines its visual and language understanding to generate relevant and informative responses. Additionally, try using the model in different contexts, such as educational or industrial applications, to explore its versatility and potential use cases.

Read more

Updated Invalid Date

🎲

DeepSeek-V2-Chat

deepseek-ai

Total Score

335

The DeepSeek-V2-Chat model is a text-to-text AI assistant developed by deepseek-ai. It is similar to other large language models like DeepSeek-V2, jais-13b-chat, and deepseek-vl-7b-chat, which are also designed for conversational tasks. Model inputs and outputs The DeepSeek-V2-Chat model takes in text-based inputs and generates text-based outputs, making it well-suited for a variety of language tasks. Inputs Text prompts or questions from users Outputs Coherent and contextually-relevant responses to the user's input Capabilities The DeepSeek-V2-Chat model can engage in open-ended conversations, answer questions, and assist with a wide range of language-based tasks. It demonstrates strong capabilities in natural language understanding and generation. What can I use it for? The DeepSeek-V2-Chat model could be useful for building conversational AI assistants, chatbots, and other applications that require natural language interaction. It could also be fine-tuned for domain-specific tasks like customer service, education, or research assistance. Things to try Experiment with the model by providing it with a variety of prompts and questions. Observe how it responds and note any interesting insights or capabilities. You can also try combining the DeepSeek-V2-Chat model with other AI systems or data sources to expand its functionality.

Read more

Updated Invalid Date

🛸

deepseek-llm-67b-chat

deepseek-ai

Total Score

164

deepseek-llm-67b-chat is a 67 billion parameter language model created by DeepSeek AI. It is an advanced model trained on a vast dataset of 2 trillion tokens in both English and Chinese. The model is fine-tuned on extra instruction data compared to the deepseek-llm-67b-base version, making it well-suited for conversational tasks. Similar models include the deepseek-coder-6.7b-instruct and deepseek-coder-33b-instruct models, which are specialized for code generation and programming tasks. These models were also developed by DeepSeek AI and have shown state-of-the-art performance on various coding benchmarks. Model inputs and outputs Inputs Text Prompts**: The model accepts natural language text prompts as input, which can include instructions, questions, or statements. Chat History**: The model can maintain a conversation history, allowing it to provide coherent and contextual responses. Outputs Text Generations**: The primary output of the model is generated text, which can range from short responses to longer form paragraphs or essays. Capabilities The deepseek-llm-67b-chat model is capable of engaging in open-ended conversations, answering questions, and generating coherent text on a wide variety of topics. It has demonstrated strong performance on benchmarks evaluating language understanding, reasoning, and generation. What can I use it for? The deepseek-llm-67b-chat model can be used for a variety of applications, such as: Conversational AI Assistants**: The model can be used to power intelligent chatbots and virtual assistants that can engage in natural dialogue. Content Generation**: The model can be used to generate text for articles, stories, or other creative writing tasks. Question Answering**: The model can be used to answer questions on a wide range of topics, making it useful for educational or research applications. Things to try One interesting aspect of the deepseek-llm-67b-chat model is its ability to maintain context and engage in multi-turn conversations. You can try providing the model with a series of related prompts and see how it responds, building upon the prior context. This can help showcase the model's coherence and understanding of the overall dialogue. Another thing to explore is the model's performance on specialized tasks, such as code generation or mathematical problem-solving. By fine-tuning or prompting the model appropriately, you may be able to unlock additional capabilities beyond open-ended conversation.

Read more

Updated Invalid Date