chavinlo

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gpt4-x-alpaca

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Here's a recommendation from chatGPT >

To set up the inference code for text generation using transformers in Python, follow these steps:

1. Import the necessary libraries:

import torch
import transformers

2. Load the pre-trained model and tokenizer:

model = transformers.AutoModelForCausalLM.from_pretrained("model_name")
tokenizer = transformers.AutoTokenizer.from_pretrained("model_name")

Replace "model_name" with the name of the pre-trained model you want to use. You can find a list of pre-trained models at https://huggingface.co./models.

3. Set the device to run on (GPU or CPU):

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

4. Define the function to generate text:

def generate_text(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    input_ids = input_ids.to(device)
    output = model.generate(input_ids=input_ids, max_length=50)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

Here, input_text is the prompt for the text generation, and max_length is the maximum length of the generated text.

5. Test the function:

input_text = "Hello, how are you?"
generated_text = generate_text(input_text)
print(generated_text)

This will generate and print the text based on the input prompt.

Note: The above code is a simplified version of the inference code. You may need to modify it based on the specific requirements of your project.