codellama-34b-python

Maintainer: meta

Total Score

6

Last updated 6/21/2024
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Model overview

codellama-34b-python is a 34 billion parameter language model developed by Meta that has been fine-tuned for coding with Python. It is part of the Code Llama family of models, which also includes variants with 7 billion and 13 billion parameters, as well as instruction-following variants. These models are based on the Llama 2 language model and show improvements on inputs with up to 100k tokens. The Code Llama - Python and Code Llama models are not fine-tuned for instruction following, while the Code Llama - Instruct models have been specifically trained to follow programming-related instructions.

Model inputs and outputs

codellama-34b-python takes text prompts as input and generates continuations of that text. The model supports input sequences up to 100,000 tokens long and can be used for a variety of programming-related tasks, including code generation, code completion, and code understanding.

Inputs

  • Prompt: The text prompt to be continued by the model.
  • Max Tokens: The maximum number of tokens to be generated in the output.
  • Temperature: A value controlling the randomness of the generated output, with lower values producing more deterministic and coherent text.
  • Top K: The number of most likely tokens to consider during sampling.
  • Top P: The cumulative probability threshold to use for sampling, which can help control the diversity of the generated output.
  • Repeat Penalty: A value that penalizes the model for repeating the same tokens, encouraging more diverse output.
  • Presence Penalty: A value that penalizes the model for generating tokens that have already appeared in the output, also encouraging diversity.
  • Frequency Penalty: A value that penalizes the model for generating tokens that are already highly frequent in the output, further encouraging diversity.

Outputs

  • Generated Text: The continuation of the input prompt, generated by the model.

Capabilities

codellama-34b-python has been fine-tuned on a large corpus of Python code and can generate coherent and relevant Python code given a prompt. It can be used for tasks like code completion, code generation, and code understanding. The model also has strong language understanding capabilities and can be used for general text generation and understanding tasks.

What can I use it for?

You can use codellama-34b-python for a variety of programming-related tasks, such as automating code generation, assisting with code refactoring and debugging, or even generating educational content and tutorials. The model's large size and strong performance make it a powerful tool for developers, researchers, and businesses looking to leverage large language models for coding and software engineering tasks.

Things to try

One interesting capability of codellama-34b-python is its ability to perform code infilling, where the model can generate missing code segments based on the surrounding context. This can be useful for tasks like automated code refactoring or code completion. You can also experiment with different prompting techniques to see how the model responds to various programming-related instructions and queries.



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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