gpt quickstart

Sure, here's a quickstart guide for using GPT:

1. Choose a GPT model: There are several pre-trained GPT models available, such as GPT-2, GPT-3, and so on. Choose the one that best suits your needs.

2. Install the necessary libraries: You will need to install the transformers library, which provides access to the pre-trained GPT models.

3. Load the pre-trained model: Use the transformers library to load the pre-trained GPT model of your choice.

4. Generate text: Use the generate() method of the loaded model to generate text based on the input prompts. You can control the length and other parameters of the generated text.

5. Fine-tune the model (optional): If you want to customize the pre-trained model for your specific use case, you can fine-tune it on your own dataset.

Here's some sample code to generate text using GPT-2:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Set the input prompt
prompt = "Once upon a time"

# Generate text based on the input prompt
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, max_length=50, do_sample=True)

# Print the generated text
print(tokenizer.decode(output[0], skip_special_tokens=True))

This will generate 50 tokens of text starting with the input prompt "Once upon a time" using the GPT-2 model.