Phind-CodeLlama-34B-Python-v1

Maintainer: Phind

Total Score

249

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 Phind-CodeLlama-34B-v1 model is a fine-tuned version of the CodeLlama-34B and CodeLlama-34B-Python models, achieving 67.6% and 69.5% pass@1 on the HumanEval benchmark respectively. This exceeds the performance of GPT-4, which achieves 67% on the same benchmark. The model was fine-tuned by Phind on a proprietary dataset of 80k high-quality programming problems and solutions, using decontamination techniques to ensure the validity of the results.

Model inputs and outputs

This model is a text-to-text AI assistant, taking in user prompts and generating relevant text responses. It is somewhat instruction-tuned, but not fully chat-tuned, so users should avoid using the Llama chat markup and instead simply provide their task or request followed by \n: .

Inputs

  • User prompts or instructions for the model, such as "Write me a linked list implementation:\n"

Outputs

  • Textual responses from the model, such as a linked list implementation in code form.

Capabilities

The Phind-CodeLlama-34B-v1 model is a capable code generation and understanding model, excelling at tasks like code completion, infilling, and following programming instructions. It has been trained to be proficient in Python, as well as other programming languages like C/C++, TypeScript, and Java.

What can I use it for?

This model could be useful for a variety of software development and programming tasks, such as:

  • Generating boilerplate code or code snippets
  • Assisting with programming problem-solving and debugging
  • Translating between different programming languages
  • Automating repetitive coding tasks

However, as the Phind team notes, the model has undergone limited testing and additional safety measures should be taken before deploying it in real-world applications.

Things to try

One interesting aspect of this model is its use of instruction-tuning rather than traditional chat-based prompting. This makes it better suited for task-oriented interactions, where the user provides a clear request or instruction, rather than open-ended conversations. Experiment with providing the model with concise, well-defined programming tasks and see how it responds.



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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