CodeLlama-34B-GGUF
Maintainer: TheBloke
55
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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
The CodeLlama-34B-GGUF is a 34 billion parameter large language model created by Meta and maintained by TheBloke. It is part of the CodeLlama family of models, which also includes 7B and 13B versions. The CodeLlama models are designed for code synthesis and understanding, with variants specialized for Python and instruction following. This 34B GGUF version provides quantized model files for efficient CPU and GPU inference.
Model inputs and outputs
Inputs
- Text: The model takes text inputs to generate new text.
Outputs
- Text: The model outputs generated text, which can be used for a variety of tasks such as code completion, infilling, and chat.
Capabilities
The CodeLlama-34B-GGUF model is capable of general code synthesis and understanding. It can be used for tasks like code completion, where it can generate the next lines of code based on a prompt, as well as code infilling, where it can fill in missing parts of code. The model also has capabilities for instruction following and chat, making it useful for building AI assistants.
What can I use it for?
The CodeLlama-34B-GGUF model can be used for a variety of applications, such as building code editors or AI programming assistants. Developers could use the model to autocomplete code, generate new functions or classes, or explain code snippets. The instruction-following capabilities also make it useful for building chatbots or virtual assistants that can help with programming tasks.
Things to try
One interesting thing to try with the CodeLlama-34B-GGUF model is to provide it with a partially completed code snippet and see how it can fill in the missing parts. You could also try giving it a high-level description of a programming task and see if it can generate the necessary code to solve the problem. Additionally, you could experiment with using the model for open-ended conversations about programming concepts and techniques.
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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