Quick Start Guide: Using Pythia Models Hosted on HuggingFace for Causal Language Generation with GPTNeoX Library

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Well, you’ve come to the right place! In this quick start guide, we’ll show you how to do just that using Pythia and GPTNeoX.

Before anything else: make sure you have Python installed (duh). Then, head on over to Hugging Face and download the latest version of the GPTNeoX library. Don’t worry if you don’t know what any of those words mean we’ll explain it all in a sec.

Once you’ve got that sorted out, open up your favorite text editor (or IDE) and create a new Python file called `causal_language_generator.py`. This is where the magic happens!

Before anything else: import the necessary libraries. We’ll be using PyTorch for our neural network framework, and Hugging Face’s transformers library to handle loading and preprocessing of data.

# Import necessary libraries
import torch # Importing PyTorch library for neural network framework
from transformers import AutoTokenizer, GPTNeoXForCausalLM # Importing Hugging Face's transformers library for data loading and preprocessing

# Define a function to generate causal language
def generate_causal_language(input_text):
    # Load pre-trained tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") # Loading pre-trained tokenizer for GPT-Neo 2.7B model
    model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B") # Loading pre-trained GPT-Neo 2.7B model for causal language generation

    # Encode input text using tokenizer
    input_ids = tokenizer.encode(input_text, return_tensors="pt") # Encoding input text into numerical tokens using tokenizer

    # Generate output text using model
    output = model.generate(input_ids, do_sample=True, max_length=100) # Generating output text using GPT-Neo 2.7B model with a maximum length of 100 tokens

    # Decode output text using tokenizer
    output_text = tokenizer.decode(output[0], skip_special_tokens=True) # Decoding output tokens into text using tokenizer and removing special tokens

    # Print generated causal language
    print(output_text)

# Call the function with input text
generate_causal_language("The sun is shining and the birds are chirping.") # Calling the function to generate causal language based on the given input text

Next, let’s load in the Pythia model we want to use for causal language generation. This is where things get a little tricky you need to know which specific version of Pythia you want to use (there are many!), and how to download it from Hugging Face. For this example, let’s say we want to use the “pythia-large” model hosted at `huggingface/transformers`.

# Import the necessary libraries
from transformers import AutoTokenizer, GPTNeoXForCausalLM

# Create a tokenizer object using the AutoTokenizer class and specify the model to be used from the "huggingface/transformers" repository
tokenizer = AutoTokenizer.from_pretrained("huggingface/transformers")

# Create a model object using the GPTNeoXForCausalLM class and specify the model to be used from the "huggingface/pythia-large" repository
# Set the trust_remote_code parameter to True to allow downloading the model from the remote repository
model = GPTNeoXForCausalLM.from_pretrained("huggingface/pythia-large", trust_remote_code=True)

Now that we’ve loaded in our model, let’s define a function to generate some causal language! This is where the real magic happens PyTorch and GPTNeoX will handle all of the heavy lifting for us.



# Define a function to generate causal language using a given prompt
def generate_causal_language(prompt):
    # Preprocess our input prompt using Hugging Face's tokenizer library
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Set up our model and move it to the GPU (if available)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device) # Move the model to the specified device (GPU or CPU)
    
    # Generate some causal language using PyTorch's autoregressive decoding algorithm
    with torch.no_grad():
        for i in range(10):
            output = model(inputs["input_ids"].unsqueeze(0).to(device), labels=None) # Generate output using the model and input prompt
            next_token = output[0][-1].argmax().item() # Get the index of the highest probability token from the output
            
            # Append our generated token to the input prompt and continue generating causal language!
            inputs["input_ids"][:, -1] = next_token # Append the generated token to the end of the input prompt
    
    # Convert our output back into a string for easy reading
    return tokenizer.decode(inputs["input_ids"].squeeze().tolist(), skip_special_tokens=True) # Convert the output back into a string, removing any special tokens

And that’s it! You can now call the `generate_causal_language()` function with any input prompt you like, and watch as Pythia generates some fancy causal language for you.

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