To use ChatGPT, you’ll need to load a pre-trained model. Here are the steps to load the pre-trained model using the Transformers package:
1. Import the necessary modules:
`import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
2. Load the pre-trained tokenizer:
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
This will load the pre-trained GPT-2 tokenizer, which is used to convert text into numerical inputs that can be fed into the GPT-2 model.
3. Load the pre-trained GPT-2 model:
model = GPT2LMHeadModel.from_pretrained('gpt2', pad_token_id=tokenizer.eos_token_id)
This will load the pre-trained GPT-2 model, which is used to generate text based on the input provided by the tokenizer. The `pad_token_id` argument specifies the token ID to use for padding the input sequences.
4. Set the device:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
This will set the device to use for running the model. If a CUDA-enabled GPU is available, the model will be run on the GPU for faster performance. Otherwise, it will be run on the CPU.
Once you’ve loaded the pre-trained model, you can use it to generate textusing the `generate()` method. Here’s an example of how to use the `generate()` method:
input_text = "Hello, how are you?" input_ids = tokenizer.encode(input_text, return_tensors='pt') input_ids = input_ids.to(device) output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2) output_text = tokenizer.decode(output[0], skip_special_tokens=True) print(output_text)
In this example, we first encode the input text using the tokenizer and convert it to a PyTorch tensor. We then move the input tensor to the device (CPU or GPU) specified earlier. Finally, we generate text using the `generate()` method, which takes the input tensor, maximum length of the generated text, number of beams to use for beam search, and the size of the n-grams to avoid repeating.
The `generate()` method returns a tensor containing the generated text, which we decode using the tokenizer to get the final output text. We then print the output text.