Update README.md

README.md

@@ -80,15 +80,15 @@ The `config.py` file contains critical settings for training and inference, allo
 
 ### Generative Modelling
 
-Generative modelling with bGPT is a flexible and powerful approach to learning and generating new data across various formats. bGPT segments byte sequences into patches, predicts next patch features with a patch-level decoder, and reconstructs bytes within patches using these features with a byte-level decoder. The training for bGPT primarily revolves around generative modelling through next byte prediction as its core focus, playing a pivotal role in predicting and generating bytes. Here's how to get started:
+Generative modelling with bGPT is a flexible and powerful approach to learning and generating new data across various formats. bGPT segments byte sequences into patches, predicts next patch features with a patch-level decoder, and reconstructs bytes within patches using these features with a byte-level decoder. Here's how to get started:
 
-1. **Prepare Your Data**: Since bGPT models information at the byte level, it can work with any type of file that exists on a computer, regardless of format. This means your training and evaluation datasets can include text, images, audio, or any other file type. Ensure that your datasets are ready and accessible for the model to train on and evaluate against.
+1. **Prepare Your Data**: Since bGPT models information at the byte level, it can work with any type of file that exists on a computer, regardless of format. This means your training and evaluation datasets can include text, images, audio, or any other file type.
+2. 
+3. **Adjust Configuration Settings**: Modify the `config.py` file to tailor the training process to your needs. At a minimum, you should update the `TRAIN_FOLDERS` and `EVAL_FOLDERS` to point to your actual data directories. Also, specify where to save the trained model weights and logs by setting `WEIGHTS_PATH` and `LOGS_PATH`. You may adjust other parameters based on your specific requirements. For instance, with the default `PATCH_SIZE=16` and `PATCH_LENGTH=512`, bGPT can model byte sequences up to 8KB. If your training files are larger, and you have sufficient computational resources, consider increasing these parameters to accommodate the larger file sizes.
 
-2. **Adjust Configuration Settings**: Modify the `config.py` file to tailor the training process to your needs. At a minimum, you should update the `TRAIN_FOLDERS` and `EVAL_FOLDERS` to point to your actual data directories. Also, specify where to save the trained model weights and logs by setting `WEIGHTS_PATH` and `LOGS_PATH`. You may adjust other parameters based on your specific requirements. For instance, with the default `PATCH_SIZE=16` and `PATCH_LENGTH=512`, bGPT can model byte sequences up to 8KB. If your training files are larger, and you have sufficient computational resources, consider increasing these parameters to accommodate the larger file sizes.
+4. **Leverage Pre-trained Weights (Optional)**: If you wish to fine-tune a pre-trained bGPT model, set `PRE_WEIGHTS_PATH` to the location of the pre-trained weights and ensure `LOAD_FROM_PRE_CHECKPOINT=True`. To train a model from scratch, simply set `LOAD_FROM_PRE_CHECKPOINT=False`.
 
-3. **Leverage Pre-trained Weights (Optional)**: If you wish to fine-tune a pre-trained bGPT model, set `PRE_WEIGHTS_PATH` to the location of the pre-trained weights and ensure `LOAD_FROM_PRE_CHECKPOINT=True`. To train a model from scratch, simply set `LOAD_FROM_PRE_CHECKPOINT=False`.
-
-4. **Start Training**: Run `train-gen.py` to begin the training process. The script will use the configurations set in `config.py` and apply the training data to learn generative models capable of producing new, unseen outputs in the format of your training data.
+5. **Start Training**: Run `train-gen.py` to begin the training process. The script will use the configurations set in `config.py` and apply the training data to learn generative models capable of producing new, unseen outputs in the format of your training data.
 
 ### Classification