deepseek-coder-33b-base

Maintainer: deepseek-ai

Total Score

62

Last updated 5/28/2024

💬

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

Create account to get full access

or

If you already have an account, we'll log you in

Model Overview

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 ,[object Object], the 33B parameter ,[object Object], and the 6.7B parameter ,[object Object]. 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.



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-coder-1.3b-base

deepseek-ai

Total Score

57

deepseek-coder-1.3b-base is a 1.3 billion parameter AI model developed by deepseek-ai that is specialized in code generation and completion. It was trained from scratch on 2 trillion tokens, with 87% of the data being code and the remaining 13% being natural language data in both English and Chinese. Compared to the deepseek-coder-33b-base and deepseek-coder-6.7b-base models, the 1.3 billion parameter version is more lightweight and accessible, while still providing state-of-the-art performance on multiple programming language benchmarks. Model inputs and outputs deepseek-coder-1.3b-base is a causal language model that takes in natural language or partial code as input and generates relevant text or code as output. The model can be used for a variety of code-related tasks, including code completion, code generation, and even repository-level code completion. Inputs Natural language prompts or partial code snippets Outputs Completed code snippets or generated code based on the input prompt Capabilities deepseek-coder-1.3b-base has demonstrated strong capabilities in code generation and completion, achieving state-of-the-art performance on benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. The model is able to understand and generate code in multiple programming languages, and can even complete complex, multi-line code segments based on partial inputs. What can I use it for? The deepseek-coder-1.3b-base model can be a powerful tool for developers and data scientists looking to streamline their coding workflows. Some potential use cases include: Generating boilerplate code or scaffolding for new projects Completing partially written code snippets to save time Generating code to implement specific algorithms or functionality Assisting with code refactoring and optimization Aiding in the onboarding of new developers by providing example code Things to try One interesting capability of deepseek-coder-1.3b-base is its ability to perform "repository-level" code completion, where the model can generate relevant code based on the context of an entire codebase, rather than just a single code snippet. This can be particularly useful for tasks like implementing common design patterns or integrating third-party libraries into a project. Another aspect to explore is the model's performance on domain-specific coding tasks, such as data analysis, machine learning, or web development. The model's strong natural language understanding may enable it to generate high-quality code for a variety of use cases beyond general-purpose programming.

Read more

Updated Invalid Date

👨‍🏫

deepseek-coder-6.7b-base

deepseek-ai

Total Score

72

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.

Read more

Updated Invalid Date

🔮

deepseek-coder-33b-instruct

deepseek-ai

Total Score

403

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.

Read more

Updated Invalid Date

⚙️

deepseek-coder-1.3b-instruct

deepseek-ai

Total Score

83

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.

Read more

Updated Invalid Date