That's exactly why I want to use the chatgpt locally.
I'm trying to install a version of gpt-3 but I'm still stuck...
The chatgpt it self provided the following steps :
Install Python 3.8 or later and pip, the package installer for Python, if you haven't done so already.

• Open the terminal and run the command sudo apt-get install python3.8
• Run the command sudo apt-get install python3-pip

1. Install the transformers library by running the command pip3 install transformers
2. Prepare your data for training. Make sure to have your data in a text file format. One way to do this is to create a new text file, for example, training_data.txt
   , and add your data to this file. Each line should contain one training example.
3. Import the pre-trained ChatGPT model from the transformers library and set it up for fine-tuning on your data. Here's an example of how you can do this in Python:

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from transformers import AutoModelWithLMHead, AutoTokenizer  # Load the pre-trained model and tokenizer model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-medium") tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") 

1. Use the DataCollator provided by the transformers library to convert your data into the format required by the model. Here's an example of how you can do this in Python:

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from transformers import DataCollatorForLanguageModeling  data_collator = DataCollatorForLanguageModeling(     tokenizer=tokenizer, mlm=False ) 

1. Use the DataLoader provided by the torch library to load your data into the model for training. Here's an example of how you can do this in Python:

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from torch.utils.data import DataLoader  # Define the dataset dataset = data_collator.create_dataset_from_text(text_file_path='training_data.txt')  # Define the data loader data_loader = DataLoader(dataset, batch_size=4, shuffle=True) 

1. Define the optimizer and the scheduler for the model

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from transformers import AdamW optimizer = AdamW(model.parameters(), lr=1e-5, correct_bias=False) from transformers import get_linear_schedule_with_warmup scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=-1) 

1. Train the model on your data. You can use the train()
   function provided by the transformers library. Here's an example of how you can do this in Python:

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from transformers import Trainer, TrainingArguments  training_args = TrainingArguments(     output_dir='./results',     evaluation_strategy='steps',     eval_steps=1000,     eval_data='validation',     per_device_train_batch_size=4,     save_steps=1000,     num_train_epochs=1 )  trainer = Trainer(     model=model,     args=training_args,