djliden/notebooks
Example notebooks
This collection of Jupyter notebooks provides practical guidance for training specialized large language models (LLMs). It shows how to adapt smaller models for specific tasks, starting with single-GPU setups and progressively scaling to complex multi-GPU and multi-node configurations. Machine learning engineers and researchers will find these examples useful for understanding and implementing various distributed training techniques.
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Use this if you are a machine learning engineer or researcher who needs to fine-tune language models and want to learn how to scale training from a single GPU to distributed, multi-node environments.
Not ideal if you are looking for a pre-trained, ready-to-use language model or if you are not familiar with the basics of machine learning model training.
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Jupyter Notebook
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MIT
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Last pushed
Jul 14, 2025
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