deepseek-coder-6.7b-base
Maintainer: deepseek-ai
72
👨🏫
| 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 |
Create account to get full access
Model overview
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-basemodel 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.
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
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.
Updated Invalid Date
💬
deepseek-coder-33b-base
62
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.
Updated Invalid Date
🤯
deepseek-coder-6.7b-instruct
306
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.
Updated Invalid Date
⚙️
deepseek-coder-1.3b-instruct
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.
Updated Invalid Date