The LLaMA language model implemented using Transformers has a wide range of potential use cases. One possible use case is for language generation, where the model can be used to generate human-like text for various purposes such as writing articles, generating dialogue for virtual characters, or even creating content for social media posts. Another use case is for text completion, where the model can be used to help users complete their sentences or suggest the next word or phrase based on the context. This can be beneficial for applications such as predictive typing, chatbots, or writing assistants. Additionally, the LLaMA model can be used for language understanding tasks, where it can be trained to comprehend and answer questions, perform information retrieval, or summarize text. With its ability to understand and generate human-like text, this AI model has the potential to be incorporated into a wide range of products and services, such as virtual assistants, content generation tools, or even language translation systems.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Flan T5 Xl
|Llama 2 7b
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Summary of this model and related resources.
Transformers implementation of the LLaMA language model
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|View on Github
|View on Arxiv
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run
|Nvidia A100 (40GB) GPU
|Average Completion Time