Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# harry-GPoTter
+
+harry-GPoTter is a transformer text generation model implemented in PyTorch. It has been trained on text from all 7 books from from all 7 books of the Harry Potter series. In only 10 minutes of training with the free tier of [Google Colaboratory](https://colab.research.google.com/), the model learnt to generate coherent and grammatically correct sentences.
+
+- Code and more information in the [GitHub Repository](https://github.com/ShawnLJW/harry-GPoTter) 
+- Download the [weights](https://huggingface.co/ShawnLJW/harry-GPoTter/resolve/main/checkpoint.pt)
+
+## Text Generation with harry-GPoTter
+> “Ah,” said Mrs. Weasley, hiscolored lips looking unpleasant. “He wasn’t talking about her, he has tried to think he was saying he had looked up. The bleers were flooding.”
+>
+> “My master died?” whispered Voldemort, but the wasnoddenbling until he are, making to be seeing him.
+>
+> “I’ll see you, Professor Lockhart,” said Hermione, “but so surely now to have solid on it out of her whole bed! You’re thinking —
+>
+> “Oh hello the unconscious!”
+>
+> “And now blimey,” said Harry, “it was a very serious for an enormous mother. ...”
+
+## Model Details
+
+harry-GPoTter is a relatively small language model with 56M parameters (less than 1/2x of smallest gpt-2). It contains 8 layers of 8 headed attention with a hidden size of 384. It supports a maximum sequence length of 128. For tokenization, we use the same tokenizer as text-davinci-003, which has a vocabulary of 50,280 in total.
+
+The model was trained for 2000 epochs in about 10 minutes with the free tier of Google Colab GPU Runtime. It achieves a cross-entropy loss of 3.1189.
+
+This model was built for learning purposes. You can probably get better performance by finetuning a pre-trained model.