Optimizing inference proxy for LLMs
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Updated
Sep 27, 2024 - Python
Optimizing inference proxy for LLMs
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Enhance compliance monitoring by automating the tracking of regulatory changes, internal policies, and employee activities to ensure adherence to all relevant regulations.
A simplified agentic workflow process based on the Mixture of Agents (MoA) system for Large Language Models (LLMs)
A machine learning package for streaming data in Python. The other ancestor of River.
Classifying drugs based on their biological activity.
Pythonic wrapper around Massive Online Analysis (MOA)
Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained kn…
Elucidate and visualise a compound's mechanism of action by combining structure-based target prediction with gene expression-based causal reasoning, plus pathway enrichment to put results into biological context. GUI-based (minimal coding experience required).
Trabalho de matéria de Modelagem e Otimização de Algoritmos
Simple workflow API for MOA.
MODIS Mosaic of Antarctica
Repository for the StreamingRandomPatches algorithm implemented in MOA 2019.04
My Java codes for the MOA framework. It includes the implementations of FHDDM, FHDDMS, and MDDMs.
Mechanisms of Action (MoA) Prediction https://www.kaggle.com/garywei944/pca-lr-ridge-rf-nn-tuning-hyper-parameters
UMM-Discovery is a fully unsupervised deep learning method to cluster cellular images with similar phenotypes together, solely based on the intensity values.
Test of the drift classifiers implemented in MOA
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