Скачать или смотреть 7-8 From NLP to LLMs

This episode marks a significant milestone, congratulating those who have reached this point in the curriculum, as they now possess the necessary knowledge and tools to tackle nearly any language task using 🤗 Transformers and the Hugging Face ecosystem.
While many traditional Natural Language Processing (NLP) tasks have been covered, the field has been profoundly transformed by Large Language Models (LLMs). LLMs have dramatically expanded the possibilities in language processing because they are capable of handling multiple tasks without requiring task-specific fine-tuning. They excel at following instructions, adapting to different contexts, and generating text that is coherent and contextually appropriate for various applications. Moreover, LLMs can perform reasoning and solve complex problems through advanced techniques like chain-of-thought prompting.
Despite the power of LLMs, the foundational NLP skills learned previously remain essential for effectively leveraging these advanced models. Understanding concepts such as tokenization, model architectures, fine-tuning approaches, and evaluation metrics provides the critical knowledge needed to use LLMs to their full potential.
Upon completing this rapid overview of core language tasks, participants should have a firm grasp of several key concepts:
Knowing which architectures—encoder, decoder, or encoder-decoder—are best suited for specific tasks.Understanding the difference between pretraining and fine-tuning a language model.Knowing how to train Transformer models using the Trainer API, the distributed training features of 🤗 Accelerate, or alternatives like TensorFlow and Keras.Understanding the meaning and limitations of evaluation metrics like ROUGE and BLEU for text generation tasks.Knowing how to interact with fine-tuned models, both on the Hub and by utilizing the pipeline from 🤗 Transformers.Appreciating how LLMs build upon and extend traditional NLP techniques.
This episode also acknowledges that even with comprehensive knowledge, there will be times when difficult bugs are encountered or questions arise about specific language processing problems. Fortunately, the Hugging Face community is available to assist. The concluding segment of this part of the course will focus on practical steps for debugging Transformer models and techniques for asking for help effectively.