LLaMA-Pro-8B-Instruct
Maintainer: TencentARC
58
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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
LLaMA-PRO-Instruct is a transformative expansion of the LLaMA2-7B model, now boasting 8.3 billion parameters. Developed by the Tencent ARC team, it uniquely specializes in programming, coding, and mathematical reasoning, while maintaining versatility in general language tasks. Compared to its predecessors in the LLaMA series, LLaMA-PRO-Instruct demonstrates exceptional competence, especially in code domains.
Model inputs and outputs
LLaMA-PRO-Instruct is a text-to-text model, taking text as input and generating text as output. It can handle a wide range of natural language tasks, from general language processing to specialized challenges in programming and mathematics.
Inputs
- Text prompts containing natural language instructions or queries
Outputs
- Text responses generated by the model to address the given input prompts
Capabilities
LLaMA-PRO-Instruct excels at complex NLP challenges, such as programming, mathematical reasoning, and general language processing. It can be employed for a variety of specialized and broad applications, outperforming previous LLaMA models in domains like code, while maintaining strong performance on general language tasks.
What can I use it for?
With its advanced capabilities in programming and mathematics, LLaMA-PRO-Instruct is well-suited for applications that require integration of natural and programming languages, such as code generation, question-answering, and problem-solving. Developers and researchers can leverage this model for projects in areas like AI-assisted software development, data analysis, and scientific computing.
Things to try
Experiment with LLaMA-PRO-Instruct on a range of tasks, from writing code snippets to solving complex math problems. Observe how it handles both specialized and general language challenges, and explore ways to incorporate its capabilities into your own applications.
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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