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thisisakz/WOOGPT
WOOGPT is a machine learning model from thisisakz. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
WOOGPT is a custom language model fine-tuned on Wizard of Oz books and movie scripts. It aims to generate whimsical, story-rich, and character-driven text in the tone of L. Frank Baum’s magical universe.
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Updated Jun 16, 2025
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From the Hugging Face model README
WOOGPT is a custom language model fine-tuned on Wizard of Oz books and movie scripts. It aims to generate whimsical, story-rich, and character-driven text in the tone of L. Frank Baum’s magical universe.
| Hyperparameter | Value |
|---|---|
| Batch size | 64 |
| Context length | 128 tokens |
| Max iterations | 200 |
| Evaluation interval | 100 iters |
| Learning rate | 3e-4 |
| Embedding dim | 384 |
| # of heads | 8 |
| # of layers | 8 |
| Dropout | 0.2 |
| Optimizer | AdamW |
| Scheduler | Cosine |
Note: Training was performed on Wizard of Oz text data including books and screenplay dialogue. Training used a causal language modeling objective with teacher forcing.
WOOGPT was trained on:
Training epochs: 3000
Tokenizer: GPT-2 tokenizer (byte-level BPE)
Device: MPS and CUDA
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("thisisakz/WOOGPT")
tokenizer = AutoTokenizer.from_pretrained("thisisakz/WOOGPT")
prompt = "Dorothy looked at the yellow brick road and said,"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))