Cass-Beta1.3: From-Scratch Meme-Teen AI Transformer

Cass-Beta1.3 is a fully-from-scratch Transformer language model with a PG-13 meme-teen personality. It does not use any pretrained weightsโ€”all knowledge comes from auto-generated personality prompts and adaptive learning from user interactions.


Model Overview

  • Architecture: GPT-2 style Transformer (GPT2LMHeadModel)
  • Parameters: Small and lightweight (~1 million parameters) suitable for 12 GB GPU
  • Tokenizer: Custom BPE tokenizer trained from scratch
  • Training Data:
    • 100 auto-generated personality prompts (PG-13, meme-teen)
    • Incrementally updated with user chat memory for adaptive learning
  • Personality: Funny, chill, slang-heavy, PG-13
  • Memory Learning: Model fine-tunes itself every 10 user messages, adapting to user style

Intended Use

  • Personal chatbot with a meme-teen style
  • Text generation for PG-13 contexts
  • Educational/demo purposes for small-scale Transformer training

Limitations

  • Small parameter count โ†’ limited reasoning capability
  • Slang-heavy personality may produce nonsensical or repetitive output
  • Memory learning is local to user interactions; may overfit short-term style
  • Lookup functionality is simulated; no live web access

Files Included

File Description
pytorch_model.bin Model weights (from scratch)
config.json Model configuration and hyperparameters
tokenizer.json Custom BPE tokenizer
tokenizer_config.json Tokenizer configuration for Hugging Face
special_tokens_map.json Mapping for special tokens (<pad>, <s>, </s>, <unk>)
cass_memory.json Optional saved user chats for adaptive learning

Usage Example

from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast

# Load model
model = GPT2LMHeadModel.from_pretrained("DSDUDEd/Cass-Beta1.3")
tokenizer = PreTrainedTokenizerFast.from_pretrained("DSDUDEd/Cass-Beta1.3")

# Encode user input
input_text = "yo cass, what's up?"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate reply
outputs = model.generate(**inputs, max_length=32, do_sample=True, temperature=0.8)
reply = tokenizer.decode(outputs[0])

print("Cass:", reply)
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