Fine-tuning LLMs locally: A step-by-step guide

1. examples

   1 177 8,144 6.9 Jupyter Notebook

   TensorFlow examples (by tensorflow)

   

   Installation of PyTorch or TensorFlow

2. Stream

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3. CoreNLP

   2 12 9,934 9.3 Java

   CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

   

   A suitable LLM library such as Hugging Face's Transformers or Stanford's CoreNLP

4. Pytorch

   3 393 91,534 10.0 Python

   Tensors and Dynamic neural networks in Python with strong GPU acceleration

   

   Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:

5. transformers

   4 218 147,112 10.0 Python

   🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

   

   The first step is to prepare your data for fine-tuning. This usually involves tokenizing your text and converting it into a format that the LLM can understand. Here's an example using the Transformers library:

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