CodeLlama-34b-Python-hf
Maintainer: codellama
93
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
CodeLlama-34b-Python-hf is a 34 billion parameter language model from the Code Llama family, fine-tuned specifically for Python code synthesis and understanding. It is part of a larger collection of Code Llama models ranging from 7 billion to 70 billion parameters, with variants designed for general code tasks, Python, and instruction following. The similar models include smaller 7B and 13B Python versions, as well as larger 70B models.
Model inputs and outputs
CodeLlama-34b-Python-hf is an autoregressive language model that takes in text input and generates text output. It is designed to excel at code-related tasks such as code completion, infilling, and instruction following.
Inputs
- Text prompts
Outputs
- Generated text, including code snippets and responses to instructions
Capabilities
The CodeLlama-34b-Python-hf model is highly capable at a variety of code-related tasks. It can generate original code, complete partially written code, and even follow natural language instructions to write code. The model's Python specialization allows it to handle the Python programming language particularly well.
What can I use it for?
CodeLlama-34b-Python-hf and the broader Code Llama family of models are intended for commercial and research use in programming-related applications. The Python-specialized variant could be used to build interactive code assistants, augment developer productivity through code completion, or generate synthetic training data for machine learning. The Responsible Use Guide provides important guidance on the safe and ethical deployment of these models.
Things to try
One interesting aspect of CodeLlama-34b-Python-hf is its ability to seamlessly mix natural language and code. You could prompt the model with a partially written Python function and ask it to continue the implementation, or provide a high-level description of a task and have the model generate the corresponding code. The model's strong performance on Python-specific constructs makes it a powerful tool for automating code-related workflows.
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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