Professor Codephreak

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Professor Codephreak Software Engineer Machine Learning Platform Architect

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Hackathon Challenge:

OpenAI Assistants API Llama-Index/MongoDB In this hackathon, you will build and iterate on an LLM-based application using AI observability to validate the performance of your app. You can choose between two sets of tools for building your app: Tool set 1: The OpenAI Assistants API Tool set 2: Llama-Index, MongoDB and GPT-4. With either choice, you will use TruLens to validate and improve the performance of your application. By bringing together TruEra, OpenAI, Llama-Index, and […]

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together ai

aGLM MASTERMIND RAGE Mixtral8x7B playground 1

together.ai provides a cloud environment playground for a number of LLM including Mixtral8x7Bv1. This model was chosen for the 32k ++ context window and suitable point of departure dataset for deployment of aGLM Autonomous General Learning Model. aGLM design goals include RAGE with MASTERMIND controller for logic and reasoning. The following three screenshots show the first use of aGLM recognising aGLM and MASTERMIND RAGE components to include machine.dreaming and knowledge as THOT from aGLM parse. […]

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Fine-tuning Hyperparameters: exploring Epochs, Batch Size, and Learning Rate for Optimal Performance

Epoch Count: Navigating the Training Iterations The Elusive “Optimal” Settings and the Empirical Nature of Tuning It is paramount to realize that there are no universally “optimal” hyperparameter values applicable across all scenarios. The “best” settings are inherently dataset-dependent, task-dependent, and even model-dependent. Finding optimal hyperparameters is fundamentally an empirical search process. It involves: finetunegem_agent is designed to facilitate this experimentation by providing command-line control over these key hyperparameters, making it easier to explore different […]

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