deepseek-llm-67b-base
Maintainer: deepseek-ai
102
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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deepseek-llm-67b-chat
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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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deepseek-llm-7b-chat
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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.
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deepseek-coder-6.7b-base
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The deepseek-coder-6.7b-base is a 6.7 billion parameter AI model developed by DeepSeek that has been trained on a massive dataset of 2 trillion tokens, with 87% of the data being code and 13% natural language in both English and Chinese. DeepSeek offers various sizes of this code model, ranging from 1 billion to 33 billion parameters, allowing users to choose the setup most suitable for their requirements. This model aims to provide state-of-the-art performance on a range of programming language tasks and benchmarks, including HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. The model utilizes a window size of 16,000 tokens and a fill-in-the-blank task during pretraining to support project-level code completion and infilling. Model inputs and outputs Inputs Natural language prompts**: The model can accept natural language prompts, such as instructions or descriptions of a programming task. Code snippets**: The model can also take existing code snippets as input, to provide completion or modification suggestions. Outputs Generated code**: The primary output of the deepseek-coder-6.7b-base model is generated code in a variety of programming languages, based on the input prompt or seed code. Code explanations**: The model can also provide natural language explanations or descriptions of the generated code. Capabilities The deepseek-coder-6.7b-base model excels at a range of programming-related tasks, including code completion, code generation, and code understanding. For example, you can use the model to autocomplete lines of code, generate new functions or algorithms based on a description, or explain the purpose and behavior of a given code snippet. What can I use it for? The versatility of the deepseek-coder-6.7b-base model makes it a valuable tool for developers, data scientists, and anyone working with code. Some potential use cases include: Productivity enhancement**: Use the model to speed up coding tasks by providing intelligent code completion and generation. Prototyping and ideation**: Generate new code ideas or experiments based on natural language prompts. Educational and training purposes**: Utilize the model to help teach programming concepts or provide explanations of code. Code refactoring and maintenance**: Leverage the model's understanding of code to suggest improvements or modifications to existing codebases. Things to try One interesting aspect of the deepseek-coder-6.7b-base model is its ability to perform project-level code completion and infilling tasks. This means the model can understand the context and structure of larger code projects, not just individual snippets. Try providing the model with a partial or incomplete code file and see if it can intelligently fill in the missing pieces or suggest relevant additions. Another interesting experiment would be to compare the performance of the different model sizes offered by DeepSeek, from 1 billion to 33 billion parameters. Observe how the model's capabilities scale with increased size and determine the optimal tradeoff between performance and resource requirements for your specific use case.
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deepseek-coder-33b-base
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deepseek-coder-33b-base is a 33B parameter model with Grouped-Query Attention trained on 2 trillion tokens, including 87% code and 13% natural language in both English and Chinese. It is part of the DeepSeek Coder series, which offers various model sizes from 1B to 33B parameters to suit different user requirements. DeepSeek Coder models have shown state-of-the-art performance on multiple programming language benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. Similar models in the DeepSeek Coder series include the 6.7B parameter deepseek-coder-6.7b-base, the 33B parameter deepseek-coder-33b-instruct, and the 6.7B parameter deepseek-coder-6.7b-instruct. These models differ in size and whether they have been fine-tuned on instruction data in addition to the base pretraining. Model Inputs and Outputs deepseek-coder-33b-base is a language model that can generate and complete code. It takes in text prompts as input and generates relevant code completions or continuations as output. Inputs Text prompts, such as: Code stubs or partial code snippets Natural language descriptions of desired code functionality Queries about coding concepts or algorithms Outputs Completed or generated code, such as: Filled-in code to complete a partial snippet Novel code to implement a requested functionality Explanations of coding concepts or algorithms Capabilities deepseek-coder-33b-base demonstrates advanced code generation and completion capabilities, supported by its large-scale pretraining on a vast corpus of code and text data. It can assist with a variety of coding tasks, from implementing algorithms to explaining programming constructs. For example, the model can take a prompt like "#write a quick sort algorithm" and generate a complete Python implementation of the quicksort algorithm. It can also fill in missing parts of code snippets to complete the functionality. What Can I Use It For? deepseek-coder-33b-base can be leveraged for a wide range of applications that involve programming and code generation. Some potential use cases include: Developing intelligent code editors or IDEs that offer advanced code completion and generation features Building chatbots or virtual assistants that can engage in dialog about coding and provide programming help Automating repetitive coding tasks by generating boilerplate code or implementing common algorithms Enhancing software development productivity by assisting programmers with coding tasks The model's scalability and strong performance make it well-suited for commercial use cases that require robust code generation capabilities. Things to Try One interesting aspect of deepseek-coder-33b-base is its ability to work at the repository level, generating code that is coherent and consistent with the overall context of a codebase. You can try providing the model with a larger code context, such as imports, function definitions, and other supporting code, and see how it generates new functionality that seamlessly integrates with the existing structure. Another area to explore is the model's handling of more complex coding challenges, such as implementing data structures and algorithms. You can provide it with prompts that require reasoning about edge cases, optimizations, and other advanced programming concepts to see the depth of its capabilities.
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