WICKED4950/BwETA-IID-100M

# BwETA-IID-100M BwETA (Boring's Experimental Transformer for Autoregression) is a small but feisty autoregressive model trained to predict the next token in a sequence. It might not be the best, but hey, it works!

Trained on determination, fueled by suffering, powered by free TPUs. 🔥

## 🛠️ Model Details:

• —Size: 100M parameters
• —Training Data: 8M sentence (token length: 512)
• —Max Window Size: 512 tokens (It can handle bigger sequence but trained on 512 length)
• —Architecture: Transformer-based
• —Tokenizer: GPT-2 Tokenizer
• —Trainer: Custom-built because why not?

## ⚡ How to Use:

  import BwETA #use v0.12

  # Load the model from Hugging Face  
  BwETA.load_hf("WICKED4950/BwETA-IID-100M")

  # Load the model locally  
  BwETA.load_local(path)

  # Save the model locally  
  model.save_pretrained(path)

  # Generate text  
  model.custom_generate()  # (Will be changed to model.generate() in future updates)

## 📌 Notes:

• —This model is experimental and has basic functionalities.
• —If it breaks, don’t cry—fix it (or let me know).
• —You can extend its functionalities in your own code.

## 📩 Contact Me If something doesn’t work or you just wanna chat about AI, hit me up on Instagram: Instagram

## What's Next? 🚀 The future is uncertain... but it's going to be wild!

• —Possibly a 400M model—same architecture, but with more functionality.
• —Exploring new architectures & designing custom layers (because why not?).
• —Losing my sanity along the way? Most likely. But that’s the fun part. 😆