LMQL is a programming language for LLM interaction. | LMQL
LMQL is a programming language for LLMs. Robust and modular LLM prompting using types, templates, constraints and an optimizing runtime. @lmql.query def meaning_of_life(): '''lmql # top-level strings are prompts "Q: What is the answer to life, the \ universe and everything?" # generation via (constrained) variables "A: [ANSWER]" where \ len(ANSWER) < 120 and STOPS_AT(ANSWER, ".") # results are dir
GitHub - anhaidgroup/deepmatcher: Python package for performing Entity and Text Matching using Deep Learning.
DeepMatcher is a Python package for performing entity and text matching using deep learning. It provides built-in neural networks and utilities that enable you to train and apply state-of-the-art deep learning models for entity matching in less than 10 lines of code. The models are also easily customizable - the modular design allows any subcomponent to be altered or swapped out for a custom imple
API design for machine learning software: experiences from the scikit-learn project
Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and p
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GitHub - uber/orbit: A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
GluonTS: Probabilistic Time Series Models in Python
GitHub - awslabs/gluonts: Probabilistic time series modeling in Python
https://conference.scipy.org/proceedings/scipy2010/pdfs/seabold.pdf
https://conference.scipy.org/proceedings/scipy2015/pdfs/matthew_rocklin.pdf
