AIGym / TinyGPT2-81M

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This tiny model is an 81 million perimeter GPT2 based model it was trained from scratch on the 3060 TI. it uses the GPT2 tokenizer from the GPT2 repo here on hugging face.

We are training our own tokenizer from scratch and will release a version 2 of this trained on even more data sets once that is complete.

This model is in float 32 but will be converted shortly to float16 in bfloat16.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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How to Get Started with the Model

Use the code below to get started with the model.

Inference Code:

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load fine-tuned GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained("AIGym/TinyGPT2-81M-colab") # or change the name to the checkpoint if you wanted to try them out
tokenizer = GPT2Tokenizer.from_pretrained("AIGym/TinyGPT2-81M-colab") # use the same as the one above unless you know what you are doing

# Example prompts
prompts = [
    "Artificial intelligence is",
    "The future of humanity depends on",
    "In a galaxy far, far away, there lived",
    "To be or not to be, that is",
    "Once upon a time, there was a"
]

# Function to generate text based on a prompt
def generate_text(prompt, max_length=120, temperature=0.3):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
    output = model.generate(input_ids, attention_mask=attention_mask, max_length=max_length, temperature=temperature, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Generate and print completions for each prompt
for prompt in prompts:
    completion = generate_text(prompt)
    print("Prompt:", prompt)
    print("Completion:", completion)
    print()

Training Details

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019) .

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