Professor Codephreak

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Professor Codephreak Software Engineer Machine Learning Platform Architect

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GraphRAG Evolves:

Understanding PathRAG and the Future of the Retrieval Augmented Generation Engine Retrieval Augmented Generative Engine (RAGE) has enhanced how we interact with large language models (LLMs). Instead of relying solely on the knowledge baked into the model during training, RAG systems can pull in relevant information from external sources, making them more accurate, up-to-date, and trustworthy. But traditional RAG, often relying on vector databases, has limitations. A new approach, leveraging knowledge graphs, is rapidly evolving, and […]

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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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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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