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

Maintainer: deepseek-ai

Total Score

88

Last updated 5/28/2024

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

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Model overview

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.



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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