Learning mostly! There is another post about another language model using RNN so i had chaptgpt have a look at it: just by showing it the 3 source files: > T

Learning mostly! There is another post about another language model using RNN so i had chaptgpt have a look at it: just by showing it the 3 source files: > The first code file defines the architecture of the model, including the custom operations and classes used to handle different float precision modes and device placement. The second code file sets the hyperparameter settings and loads the pre-trained model's weights. The third code file defines several utility functions and classes to tokenize input text, refine and process text input, and sample the next token in a sequence given the current context. >The overall goal of the project is to generate text by training the model on a large dataset of text, the model can be fine-tuned on a specific task, such as language translation, text summarization, or text completion. The model can also be used for generating text in a conversational context, such as chatbots or virtual assistants, where the model generates text based on the current context, and the user's previous inputs. and > I can't determine if the code achieves the claims of the project, as the model has not been trained and run on any specific dataset and it is not clear what kind of performance results the developers were able to achieve with it. However, the approach used in the code appears to be logically sound. The code implements a standard transformer-based language model architecture, but with a custom implementation of the operations in order to handle different float precision modes and device placement, which is a common technique to optimize the performance of deep learning models. The code also includes some utility functions and classes that handle tokenization, text processing, and sampling of the model's output, which are also common in large-scale language model implementations. > It would be important to run the model with a large dataset of text, fine-tune it on a specific task and compare its performance with other state of the art models, to measure its quality and scaling. if you continue this road it can teach you the basics of machine learning really fast and can even get you started with a vs code dev container for it.