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"building applications with LLMs through composability".
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Making large AI models cheaper, faster and more accessible.
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Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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Framework for building applications with LLMs and Transformers (e.g. agents, semantic search, question-answering).
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Beer glass classifier created with Synaptic.
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A library for developing and comparing reinforcement learning algorithms (successor of [gym])(https://github.com/openai/gym).
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Pandas AI is a Python library that integrates LLMs capabilities into Pandas, making dataframes conversational.
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Implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
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A high-level machine learning library in the vein of Keras.
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Fast and convenient feature processing for low latency machine learning in Go.
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AI code reviewer for GitHub Actions or local use, compatible with any LLM and integrated with Jira/Linear.
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Testing framework dedicated to ML models, from tabular to LLMs. Detect risks of biases, performance issues and errors in 4 lines of code.
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A Chinese segment based on Conditional Random Field.
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Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
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This basically to gauge the understanding of Machine Learning Workflow and Regression technique in specific.
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Easy-to-use and flexible AutoML library for Python.
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Ignite your models into blazing-fast machine learning APIs with a modern framework.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Feature store for machine learning.
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FastEdit aims to assist developers with injecting fresh and customized knowledge into large language models efficiently using one single command.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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A Python package to assess and improve fairness of machine learning models.
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Visualizations for understanding and analyzing machine learning datasets.