As the original author, can you rate this ai generated summary of your video: Video tutorial on language models by Jeremy Howard from fast.ai. In the tutorial,

As the original author, can you rate this ai generated summary of your video: Video tutorial on language models by Jeremy Howard from fast.ai. In the tutorial, Howard explains the basics of language models and how to use them in practice. He starts by defining a language model as something that can predict the next word of a sentence or fill in missing words. He demonstrates this using an open AI language model called text DaVinci 003. Howard explains that language models work by predicting the probability of various possible next words based on the given context. He shows how to use language models for creative brainstorming and playing with different word predictions. He then discusses language model training and fine-tuning processes, using the ULMfit approach as an example. He explains the three steps of language model training: pre-training, language model fine-tuning, and classifier fine-tuning. He mentions the importance of fine-tuning language models for specific tasks to make them more useful. Howard also demonstrates how to use the open AI API to access language models programmatically. He shows examples of using the API to generate text, ask questions, perform code interpretation, and even extract text from images using OCR. Additionally, he discusses the options for running language models on your own computer, such as using GPUs, renting GPU servers, or utilizing cloud platforms like Kaggle and Colab. He mentions the Transformers library from Hugging Face, which provides pre-trained models and data sets for language processing tasks. He highlights the benefits of fine-tuning models and using retrieval augmented generation to combine document retrieval with language generation. The tutorial concludes with a discussion on other options for running language models, including using private GPT models, Mac-based solutions like H2O GPT and lima.cpp, and the possibility of fine-tuning models with custom data sets. Overall, the tutorial provides a comprehensive overview of language models, their applications, and different ways to use them, both with open AI models and on your own computer.