ryoungj/ObsScaling
[NeurIPS'24 Spotlight] Observational Scaling Laws
This project helps AI researchers and developers understand how large language models (LLMs) improve as they get larger or are fine-tuned. It takes existing benchmark results for various LLMs as input and outputs an analysis that predicts how a specific LLM capability (like reasoning or coding) will scale. This is for AI practitioners and researchers who are developing or evaluating LLMs.
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Use this if you need to predict the performance of new LLMs or understand the scaling behavior of emergent capabilities without extensive training.
Not ideal if you are looking for a tool to train LLMs or perform real-time inference with them.
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60
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3
Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Oct 02, 2024
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