Llama-3-8B-Instruct-Coder
Maintainer: rombodawg
51
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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 Llama-3-8B-Instruct-Coder model is an AI language model developed by Meta and uploaded by the Hugging Face user rombodawg. This model is based on the Llama-3 family of large language models and has been fine-tuned on the CodeFeedback dataset, making it specialized for coding tasks. It was trained using the Qalore method, a new training technique developed by rombodawg's colleague at Replete-AI that allows the model to be loaded on 14.5 GB of VRAM. This is a significant improvement compared to previous Llama models, which required more VRAM. The Replete-AI community, which rombodawg is a part of, is very supportive and welcoming, as described on their Discord server.
Model inputs and outputs
The Llama-3-8B-Instruct-Coder model is a text-to-text model, meaning it takes text as input and generates text as output. The model is particularly adept at understanding and generating code, thanks to its fine-tuning on the CodeFeedback dataset.
Inputs
- Text: The model can accept a variety of text-based inputs, such as natural language instructions, coding prompts, or existing code snippets.
Outputs
- Text: The model will generate text-based outputs, which can include code, explanations, or responses to the given input.
Capabilities
The Llama-3-8B-Instruct-Coder model excels at a variety of coding-related tasks, such as code completion, code generation, and code understanding. It can be used to help developers write and debug code, as well as to generate new code based on natural language descriptions. The model's capabilities have been further enhanced by the Qalore training method, which has improved its performance and efficiency.
What can I use it for?
The Llama-3-8B-Instruct-Coder model can be a valuable tool for developers, programmers, and anyone working with code. It can be used to automate repetitive coding tasks, generate boilerplate code, or even create entire applications based on high-level requirements. The model's ability to understand and generate code also makes it useful for educational purposes, such as helping students learn programming concepts or providing feedback on their code.
Things to try
One interesting thing to try with the Llama-3-8B-Instruct-Coder model is to provide it with a natural language description of a coding problem and see how it responds. You can then compare the generated code to your own solution or to the expected output, and use the model's feedback to improve your understanding of the problem and the programming concepts involved.
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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