Phind-CodeLlama-34B-v2

Maintainer: Phind

Total Score

784

Last updated 5/17/2024

🛸

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

Phind-CodeLlama-34B-v2 is a 34 billion parameter language model fine-tuned by Phind on 1.5B tokens of high-quality programming data. It achieves 73.8% pass@1 on the HumanEval benchmark, making it the current state-of-the-art open-source model for code generation. This model has been further instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use. It is comparable to other large language models like CodeLlama-13b-Instruct-hf and CodeLlama-7b-Instruct-hf from Meta, but with improved performance on programming tasks.

Model inputs and outputs

Inputs

  • Text prompts: The model accepts text prompts in the Alpaca/Vicuna instruction format, where the user provides a task description or query for the model to respond to.

Outputs

  • Generated text: The model generates fluent text completions in response to the input prompts. It can produce code snippets, explanations, and solutions to programming problems.

Capabilities

Phind-CodeLlama-34B-v2 is a powerful code generation model that can handle a variety of programming tasks, from implementing data structures in C++ to solving algorithmic problems in Python. It demonstrates strong capabilities in areas like code completion, infilling, and following natural language instructions. The model is also multilingual, with proficiency in languages like Python, C/C++, TypeScript, and Java.

What can I use it for?

This model can be used for a wide range of programming-related applications, such as building intelligent code assistants, automating code generation, and enhancing developer productivity. Potential use cases include:

  • Code completion: Suggesting relevant code snippets or completions as a developer is writing code.
  • Code generation: Generating full program solutions from high-level descriptions or requirements.
  • Prototyping and ideation: Quickly exploring different coding approaches or solutions to problems.
  • Educational tools: Assisting students in learning to code or understand programming concepts.
  • Technical content generation: Automatically producing technical documentation, tutorials, or educational materials.

Things to try

One interesting aspect of Phind-CodeLlama-34B-v2 is its ability to follow natural language instructions and generate code that meets specific requirements. For example, you could prompt the model to "Implement a linked list in C++ that supports insertion, deletion, and search operations" and it would generate a working code solution. This makes the model well-suited for building AI-powered programming assistants that can understand and execute coding tasks.

Another intriguing capability is the model's multilingual proficiency. You could try prompting it with programming problems in different languages and observe how it handles the task. This could be useful for building applications that need to work across a variety of programming languages.



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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