LightAutoML
llm_optimize
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| LightAutoML | llm_optimize | |
|---|---|---|
| 1 | 3 | |
| 767 | 43 | |
| - | - | |
| 9.2 | 5.8 | |
| about 2 years ago | 12 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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LightAutoML
llm_optimize
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Using Large Language Models for Hyperparameter Optimization, Zhang et al. 2023 [GPT-4 is quite good at finding the optimal hyperparameters for machine learning tasks]
Wrote this library as a way of doing this pretty plug and play: https://github.com/sshh12/llm_optimize
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Large Language Models as Optimizers
For anyone interested I've been working a very similar idea of LLMs for blackbox optimization: https://github.com/sshh12/llm_optimize
- Show HN: LLM Optimize, blackbox string optimization and AutoML with GPT-4
What are some alternatives?
FEDOT - Automated modeling and machine learning framework FEDOT
evalml - EvalML is an AutoML library written in python.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
dify - Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
jupyter - Jupyter metapackage for installation, docs and chat
autogluon - Fast and Accurate ML in 3 Lines of Code
lazypredict - Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Language_Identifier - Language Identification classification using XGBoost
automl - Google Brain AutoML
MLJ.jl - A Julia machine learning framework