Deepseek-ai

Models by this creator

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deepseek-coder-33b-instruct

deepseek-ai

Total Score

399

deepseek-coder-33b-instruct is a 33B parameter AI model developed by DeepSeek AI that is specialized for coding tasks. The model is composed of a series of code language models, each trained from scratch on 2T tokens with a composition of 87% code and 13% natural language in both English and Chinese. DeepSeek Coder offers various model sizes ranging from 1B to 33B parameters, enabling users to choose the setup best suited for their needs. The 33B version has been fine-tuned on 2B tokens of instruction data to enhance its coding capabilities. Similar models include StarCoder2-15B, a 15B parameter model trained on 600+ programming languages, and StarCoder, a 15.5B parameter model trained on 80+ programming languages. Model inputs and outputs Inputs Free-form natural language instructions for coding tasks Outputs Relevant code snippets or completions in response to the input instructions Capabilities deepseek-coder-33b-instruct has demonstrated state-of-the-art performance on a range of coding benchmarks, including HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. The model's advanced code completion capabilities are enabled by a large 16K context window and a fill-in-the-blank training task, allowing it to handle project-level coding tasks. What can I use it for? deepseek-coder-33b-instruct can be used for a variety of coding-related tasks, such as: Generating code snippets or completing partially written code based on natural language instructions Assisting with refactoring, debugging, or improving existing code Aiding in the development of new software applications by providing helpful code suggestions and insights The flexibility of the model's different size versions allows users to choose the most suitable setup for their specific needs and resources. Things to try One interesting aspect of deepseek-coder-33b-instruct is its ability to handle both English and Chinese inputs, making it a versatile tool for developers working in multilingual environments. You could try providing the model with instructions or prompts in both languages and observe how it responds. Another interesting avenue to explore is the model's performance on more complex, multi-step coding tasks. By carefully crafting prompts that require the model to write, test, and refine code, you can push the boundaries of its capabilities and gain deeper insights into its strengths and limitations.

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Updated 5/17/2024

🎲

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.

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Updated 5/17/2024

🤯

deepseek-coder-6.7b-instruct

deepseek-ai

Total Score

299

deepseek-coder-6.7b-instruct is a 6.7B parameter language model developed by DeepSeek AI that has been fine-tuned on 2B tokens of instruction data. It is part of the DeepSeek Coder family of code models, which are composed of models ranging from 1B to 33B parameters, all trained from scratch on a massive 2T token corpus of 87% code and 13% natural language data in English and Chinese. The DeepSeek Coder models, including the deepseek-coder-6.7b-instruct model, are designed to excel at coding tasks. They achieve state-of-the-art performance on benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS, thanks to their large training data and advanced architecture. The models leverage a 16K window size and a fill-in-the-blank task to support project-level code completion and infilling. Other similar models in the DeepSeek Coder family include the deepseek-coder-33b-instruct model, which is a larger 33B parameter version, and the Magicoder-S-DS-6.7B model, which was fine-tuned from the deepseek-coder-6.7b-base model using a novel approach called OSS-Instruct to generate more diverse and realistic instruction data. Model Inputs and Outputs Inputs Natural language instructions**: The model can take in natural language instructions or prompts related to coding tasks, such as "write a quick sort algorithm in python." Outputs Generated code**: The model outputs the generated code that attempts to fulfill the provided instruction or prompt. Capabilities The deepseek-coder-6.7b-instruct model is highly capable at a wide range of coding tasks, from writing algorithms and functions to generating entire programs. Due to its large training dataset and advanced architecture, the model is able to produce high-quality, contextual code that often performs well on benchmarks. For example, when prompted to "write a quick sort algorithm in python", the model can generate the following code: def quicksort(arr): if len(arr) pivot] return quicksort(left) + middle + quicksort(right) This demonstrates the model's ability to understand coding concepts and generate complete, working solutions to algorithmic problems. What Can I Use It For? The deepseek-coder-6.7b-instruct model can be leveraged for a variety of coding-related applications and tasks, such as: Code generation**: Automatically generate code snippets, functions, or even entire programs based on natural language instructions or prompts. Code completion**: Use the model to intelligently complete partially written code, suggesting the most relevant and appropriate next steps. Code refactoring**: Leverage the model to help refactor existing code, improving its structure, readability, and performance. Prototyping and ideation**: Quickly generate code to explore and experiment with new ideas, without having to start from scratch. Companies or developers working on tools and applications related to software development, coding, or programming could potentially use this model to enhance their offerings and improve developer productivity. Things to Try Some interesting things to try with the deepseek-coder-6.7b-instruct model include: Exploring different programming languages**: Test the model's capabilities across a variety of programming languages, not just Python, to see how it performs. Prompting for complex algorithms and architectures**: Challenge the model with more advanced coding tasks, like generating entire software systems or complex data structures, to push the limits of its abilities. Combining with other tools**: Integrate the model into your existing development workflows and tools, such as IDEs or code editors, to streamline and enhance the coding process. Experimenting with fine-tuning**: Try fine-tuning the model on your own datasets or tasks to further customize its performance for your specific needs. By exploring the full range of the deepseek-coder-6.7b-instruct model's capabilities, you can unlock new possibilities for improving and automating your coding workflows.

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Updated 5/17/2024

🛸

DeepSeek-V2

deepseek-ai

Total Score

191

DeepSeek-V2 is a text-to-image AI model developed by deepseek-ai. It is similar to other popular text-to-image models like stable-diffusion and the DeepSeek-VL series, which are capable of generating photo-realistic images from text prompts. The DeepSeek-V2 model is designed for real-world vision and language understanding applications. Model inputs and outputs Inputs Text prompts that describe the desired image Outputs Photorealistic images generated based on the input text prompts Capabilities DeepSeek-V2 can generate a wide variety of images from detailed text descriptions, including logical diagrams, web pages, formula recognition, scientific literature, natural images, and more. It has been trained on a large corpus of vision and language data to develop robust multimodal understanding capabilities. What can I use it for? The DeepSeek-V2 model can be used for a variety of applications that require generating images from text, such as content creation, product visualization, data visualization, and even creative projects. Developers and businesses can leverage this model to automate image creation, enhance design workflows, and provide more engaging visual experiences for their users. Things to try One interesting thing to try with DeepSeek-V2 is generating images that combine both abstract and concrete elements, such as a futuristic cityscape with floating holographic displays. Another idea is to use the model to create visualizations of complex scientific or technical concepts, making them more accessible and understandable.

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Updated 5/17/2024

🌐

deepseek-vl-7b-chat

deepseek-ai

Total Score

184

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.

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Updated 5/17/2024

🛸

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.

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Updated 5/17/2024

🤷

deepseek-moe-16b-chat

deepseek-ai

Total Score

105

deepseek-moe-16b-chat is a large language model developed by deepseek-ai. It is a 16 billion parameter model that has been trained on a vast corpus of text data. This model is an extension of the deepseek-moe-16b-base model, which has been further fine-tuned on additional instruction-following data to enhance its conversational and task-completion capabilities. Some other similar models developed by deepseek-ai include the deepseek-math-7b-instruct model, which is focused on math-related tasks, as well as the deepseek-llm-7b-chat and deepseek-llm-67b-chat models, which are smaller and larger versions of the conversational language model. Model Inputs and Outputs The deepseek-moe-16b-chat model is designed for open-ended text generation and can be used for a variety of natural language processing tasks, such as text completion, dialogue generation, and question answering. Inputs Text sequences**: The model accepts text sequences as input, which can be used to initiate a conversation or provide context for the model to continue generating text. Outputs Generated text**: The model outputs generated text, which can be used to continue a conversation, provide responses to questions, or generate novel content. Capabilities The deepseek-moe-16b-chat model is capable of engaging in open-ended conversations on a wide range of topics. It can understand and respond to natural language queries, generate coherent and contextually appropriate text, and even demonstrate some reasoning and analytical capabilities. For example, the model can be used to summarize articles, generate creative writing, or provide explanations for complex topics. What Can I Use It For? The deepseek-moe-16b-chat model can be used in a variety of applications, such as: Chatbots and virtual assistants**: The model can be integrated into chatbots and virtual assistants to provide natural language interactions with users. Content generation**: The model can be used to generate text for various applications, such as blog posts, marketing materials, or creative writing. Question answering**: The model can be used to answer questions on a wide range of topics, making it useful for educational or research applications. Language learning**: The model can be used to engage in conversations and provide feedback to language learners. Things to Try Some interesting things to try with the deepseek-moe-16b-chat model include: Engaging the model in open-ended conversations on a variety of topics to explore its capabilities and limitations. Providing the model with prompts or starting points for creative writing or storytelling to see what it can generate. Asking the model to perform more analytical or reasoning-based tasks, such as summarizing articles or explaining complex concepts, to assess its problem-solving abilities. Comparing the performance of the deepseek-moe-16b-chat model to other conversational AI models to understand its unique strengths and weaknesses. By experimenting with the model and exploring its various use cases, you can gain a deeper understanding of its capabilities and discover new ways to leverage its power in your own projects or applications.

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Updated 5/17/2024

🚀

deepseek-llm-67b-base

deepseek-ai

Total Score

102

The deepseek-llm-67b-base is a 67 billion parameter language model developed by DeepSeek AI. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. DeepSeek AI has also created smaller 7 billion parameter versions of their language model, including the deepseek-llm-7b-chat model, which has been fine-tuned on additional instructional data. Additionally, the company has developed a series of code-focused models called DeepSeek Coder, which range in size from 1.3 billion to 33 billion parameters and are tailored for programming tasks. Model inputs and outputs The deepseek-llm-67b-base model is a text-to-text transformer that can be used for a variety of natural language processing tasks. It takes plain text as input and generates new text as output. Inputs Text**: The model accepts any natural language text as input, such as sentences, paragraphs, or short passages. Outputs Generated Text**: The model outputs new text that continues or is relevant to the input. This can include completions, continuations, or responses to the input text. Capabilities The deepseek-llm-67b-base model has been trained on a massive corpus of text data, enabling it to engage in open-ended text generation on a wide range of topics. It can be used for tasks like question answering, summarization, translation, and creative writing. The model's large size and broad training data also allow it to demonstrate strong few-shot learning capabilities, where it can adapt to new tasks with only a small number of examples. What can I use it for? The deepseek-llm-67b-base model and its smaller variants can be used for a variety of natural language processing applications. Some potential use cases include: Content Generation**: Generating coherent and contextually relevant text for things like articles, stories, product descriptions, and marketing copy. Conversational AI**: Building chatbots and virtual assistants that can engage in natural language dialog. Summarization**: Producing concise summaries of long-form text, such as research papers or news articles. Question Answering**: Answering open-ended questions by extracting relevant information from a knowledge base. Code Generation**: The DeepSeek Coder models can be used to automatically generate, complete, and refine code snippets, as demonstrated in the provided examples. Things to try One interesting aspect of the deepseek-llm-67b-base model is its ability to generate coherent and contextually relevant text even when provided with relatively little input. This few-shot learning capability allows the model to adapt to new tasks and domains with ease. Developers could experiment with prompting the model with just a sentence or two and see how it continues the narrative or responds to the input. Additionally, the code-focused DeepSeek Coder models present an opportunity to explore more advanced programming tasks, such as generating entire functions or refactoring existing code.

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Updated 5/17/2024

🎲

deepseek-coder-7b-instruct-v1.5

deepseek-ai

Total Score

84

The deepseek-coder-7b-instruct-v1.5 is a large language model developed by DeepSeek AI, a creator focused on building advanced AI systems. This model was trained on a massive 2 trillion token dataset, with 87% code and 13% natural language in both English and Chinese. The model was first pre-trained on this large corpus using a next token prediction objective, and then fine-tuned on 2 billion tokens of instruction data to give it strong coding capabilities. Compared to similar DeepSeek Coder models like the deepseek-coder-6.7b-instruct, deepseek-coder-33b-instruct, and deepseek-coder-1.3b-base, the deepseek-coder-7b-instruct-v1.5 lands in the middle of the size spectrum at 7 billion parameters. It aims to balance powerful coding capabilities with reasonable computational requirements. Model inputs and outputs The deepseek-coder-7b-instruct-v1.5 model is a text-to-text transformer that can generate natural language responses to prompts. Its key capabilities center around coding tasks like code completion, code generation, and code understanding. Inputs Natural language prompts describing a coding task or problem Partially completed code snippets with gaps for the model to fill in Outputs Generated code to complete a given task or fill in missing code Natural language responses explaining code or providing insights Capabilities The deepseek-coder-7b-instruct-v1.5 model excels at a variety of coding-related tasks. It can generate working code for algorithms and functions, complete partially written code, and even explain coding concepts in plain language. For example, you can prompt the model to "write a quicksort algorithm in Python" and it will generate a full implementation. Or you can give it a partially written function and ask it to "fill in the missing code". Beyond just generating code, the model also demonstrates strong understanding of programming languages and concepts. You can ask it to "explain how a hash table works" or "compare the time complexity of bubble sort and quicksort", and it will provide clear and insightful explanations. What can I use it for? The deepseek-coder-7b-instruct-v1.5 model opens up a wide range of potential use cases for developers and data scientists. Some key applications include: Automating routine coding tasks like boilerplate generation, refactoring, and bug fixing Enabling more natural and conversational programming interfaces for users Powering intelligent programming assistants that can explain concepts and provide coding help Accelerating prototyping and ideation by generating starting points for new projects The model's broad capabilities also make it useful beyond just coding, such as for technical writing, documentation generation, and even creative ideation for software products. Things to try One interesting aspect of the deepseek-coder-7b-instruct-v1.5 model is its ability to work at both the granular code level and the broader project/repository level. You can prompt it with just a few lines of code and have it complete or explain that specific snippet. But you can also give it a larger codebase context, like the sample project files provided, and have it generate relevant new code or provide overall insights. This multi-scale capability allows for some unique experiments, like prompting the model with a partially written function and asking it to not just fill in the missing pieces, but to also suggest improvements or alternative implementations. Or you could have it analyze an entire project and propose higher-level refactorings or design changes. The model's strong performance on benchmarks like HumanEval, MultiPL-E, and APPS also make it an intriguing subject for further testing and exploration by the developer community.

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Updated 5/17/2024

⚙️

deepseek-coder-1.3b-instruct

deepseek-ai

Total Score

82

The deepseek-coder-1.3b-instruct model is a 1.3 billion parameter language model trained by DeepSeek AI that is specifically designed for coding tasks. It is part of the DeepSeek Coder series, which includes models ranging from 1B to 33B parameters. The DeepSeek Coder models are trained on a massive dataset of 2 trillion tokens, with 87% of the data being code and 13% being natural language text in both English and Chinese. This allows the models to excel at a wide range of coding-related tasks. Similar models in the DeepSeek Coder series include the deepseek-coder-33b-instruct, deepseek-coder-6.7b-instruct, deepseek-coder-1.3b-base, deepseek-coder-33b-base, and deepseek-coder-6.7b-base. These models offer a range of sizes and capabilities to suit different needs. Model inputs and outputs The deepseek-coder-1.3b-instruct model takes in natural language prompts and generates code outputs. The model can be used for a variety of coding-related tasks, such as code generation, code completion, and code insertion. Inputs Natural language prompts and instructions related to coding tasks Outputs Generated code in various programming languages Completed or inserted code snippets based on the input prompt Capabilities The deepseek-coder-1.3b-instruct model excels at a wide range of coding-related tasks, including writing algorithms, implementing data structures, and solving coding challenges. For example, the model can generate a quick sort algorithm in Python when given the prompt "write a quick sort algorithm". It can also complete or insert code snippets into existing code, helping to streamline the programming workflow. What can I use it for? The deepseek-coder-1.3b-instruct model can be used for a variety of applications that require coding or programming capabilities. Some potential use cases include: Developing prototypes or proofs of concept: The model can generate code to quickly test ideas and explore new concepts. Automating repetitive coding tasks: The model can assist with tasks like code formatting, refactoring, or boilerplate generation. Enhancing developer productivity: The model's code completion and insertion capabilities can help developers write code more efficiently. Educational and training purposes: The model can be used to teach programming concepts or provide feedback on coding assignments. Things to try One interesting aspect of the deepseek-coder-1.3b-instruct model is its ability to work at the project level, thanks to its large training dataset and specialized pre-training tasks. This means the model can generate or complete code that is contextually relevant to a larger codebase, rather than just producing standalone snippets. Try providing the model with a partial code file and see how it can suggest relevant completions or insertions to extend the functionality. Another interesting experiment would be to combine the deepseek-coder-1.3b-instruct model with other AI-powered tools, such as code editors or IDE plugins. This could create a powerful coding assistant that can provide intelligent, context-aware code suggestions and help streamline the development workflow.

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Updated 5/17/2024