CodeNinja-1.0-OpenChat-7B

Maintainer: beowolx

Total Score

104

Last updated 5/28/2024

📊

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 CodeNinja-1.0-OpenChat-7B is an enhanced version of the renowned openchat/openchat-3.5-1210 model. It has been fine-tuned through Supervised Fine Tuning on two expansive datasets, encompassing over 400,000 coding instructions. Designed to be an indispensable tool for coders, CodeNinja aims to integrate seamlessly into your daily coding routine.

Model inputs and outputs

Inputs

  • Code Prompts: CodeNinja maintains the same prompt structure as OpenChat 3.5, requiring users to adhere to the specified format for effective utilization.

Outputs

  • Coded Responses: CodeNinja generates detailed code responses based on the provided prompts, drawing from its extensive training data across various programming languages.

Capabilities

CodeNinja boasts several key capabilities that make it a powerful coding assistant:

  • Expansive Training Database: It has been refined with datasets from glaiveai/glaive-code-assistant-v2 and TokenBender/code_instructions_122k_alpaca_style, incorporating around 400,000 coding instructions.
  • Flexibility and Scalability: Available in a 7B model size, CodeNinja is adaptable for local runtime environments.
  • Advanced Code Completion: With a substantial context window size of 8192, it supports comprehensive project-level code completion.

What can I use it for?

Developers can leverage CodeNinja to streamline their coding workflows. It can assist with a variety of tasks, such as:

  • Generating code snippets and entire programs based on high-level descriptions
  • Providing comprehensive code completion suggestions, even for complex projects
  • Translating between different programming languages
  • Troubleshooting and debugging existing code

Things to try

One interesting aspect of CodeNinja is its ability to generate code across a wide range of programming languages. Try prompting it with tasks or descriptions that span different languages, such as Python, C++, and JavaScript, and observe how it handles the variations in syntax and semantics.

Another interesting experiment would be to explore the model's capabilities in terms of project-level code completion. Provide it with a partially completed codebase and see how it generates relevant code to fill in the gaps, taking into account the broader context.



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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