LLaMA-Pro-8B
Maintainer: TencentARC
168
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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-Pro-8B is a progressive version of the original LLaMA model, developed by Tencent's ARC Lab. It is an 8.3 billion parameter model that has been further trained on code and math corpora, totaling 80 billion tokens. This enhancement allows LLaMA-Pro-8B to specialize in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.
Compared to other models in the LLaMA series, LLaMA-Pro-8B demonstrates advanced performance across various benchmarks, outperforming the LLaMA2-7B and CodeLLaMA-7B models. It is particularly well-suited for scenarios requiring the integration of natural and programming languages.
Model inputs and outputs
Inputs
LLaMA-Pro-8Btakes text as input, which can include natural language, code, and mathematical expressions.
Outputs
- The model generates text output, including natural language, code, and mathematical expressions.
Capabilities
LLaMA-Pro-8B is designed to handle a wide range of NLP tasks, with a focus on programming, mathematics, and general language understanding. It demonstrates superior performance on benchmarks such as ARC, Hellaswag, MMLU, TruthfulQA, and Winogrande, compared to other models in the LLaMA series.
What can I use it for?
LLaMA-Pro-8B is well-suited for applications that require the integration of natural and programming languages, such as code generation, math problem-solving, and task-oriented dialogue systems. Developers can fine-tune the model on domain-specific data to further enhance its capabilities for their specific use cases.
Things to try
One interesting aspect of LLaMA-Pro-8B is its ability to handle long-form text, thanks to the Transformer blocks added to the original LLaMA model. This makes it a good candidate for tasks like multi-document question answering, long-form text summarization, and other applications that require processing and understanding of extended context.
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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