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Phi-1.5
2023年12月
The language model phi-1.5 is a Transformer with 1.3 billion parameters. It was trained using the same data sources as phi-1, augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common…
Phi-1
2023年12月
The language model phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from The Stack v1.2, Q&A content from StackOverflow, competition code from…
AutoGen
2023年9月
Enable Next-Gen Large Language Model Applications. AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They…
VISOR
2023年9月
Benchmarking Spatial Relationships in Text-to-Image Generation—Spatial understanding is a fundamental aspect of computer vision and integral for human-level reasoning about images, making it an important component for grounded language understanding. While recent large-scale text-to-image synthesis (T2I) models have shown unprecedented…
imodelsX
2022年11月
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
ToxiGen
2022年5月
Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language.…
FLAML: A Fast Library for AutoML and Tuning
2020年12月
FLAML is a Python library designed to automatically produce accurate machine learning models with low computational cost. It frees users from selecting learners and hyperparameters for each learner. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection…