mddunlap924/PyTorch-LLM
Fine-tuning an LLM using a Generic Workflow and Best Practices with PyTorch
This workflow provides a basic framework to adapt Large Language Models (LLMs) for specific tasks using PyTorch. You can input various data types, like consumer complaint text and categorical variables, to fine-tune an LLM. The output is a custom, trained LLM model capable of classifying or processing your specific data, ideal for data scientists or machine learning engineers who need to tailor LLMs for unique business problems.
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Use this if you need a flexible and well-structured PyTorch-based approach to customize Large Language Models for tasks like multi-class text classification, using your own datasets and custom layers.
Not ideal if you're looking for a low-code solution or pre-packaged Hugging Face tasks, as this workflow emphasizes custom PyTorch development and deep understanding of model architecture.
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Jul 29, 2023
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