Do you want to spice things up a bit and add some flair to your content creation game? Well, look no further because Pygmalion 7B and Metharme 7B are here to save the day (or at least make your life easier)!
First off, Pygmalion. This bad boy is a text generation model that can generate human-like responses based on given prompts or input texts. It uses a transformer architecture and has been trained on over 7 billion words of text data from various sources like books, news articles, and social media posts.
Now, Metharme. This is another text generation model that can generate responses based on given prompts or input texts. However, what sets it apart from Pygmalion is its ability to handle longer sequences of text (up to 2048 tokens) and generate more coherent and fluent output. It also uses a transformer architecture but has been trained specifically for generating news articles and other long-form content.
So, how do you use these models? Well, it’s pretty simple! First, you need to install the necessary libraries (transformers and datasets) using pip:
# This script installs the necessary libraries (transformers and datasets) using pip
# First, we need to import the necessary libraries
import transformers
import datasets
# Next, we need to use the pip command to install the libraries
pip install transformers datasets
# The above command will install the libraries globally, but it's recommended to use a virtual environment for better isolation and management of dependencies
# To create a virtual environment, we use the virtualenv command
virtualenv venv
# Then, we activate the virtual environment
source venv/bin/activate
# Now, we can install the libraries within the virtual environment
pip install transformers datasets
# Once the installation is complete, we can deactivate the virtual environment
deactivate
# Finally, we can use the libraries in our code by importing them
import transformers
import datasets
Next, download the pretrained Pygmalion 7B and Metharme 7B models from Hugging Face’s model hub:
# Import the necessary libraries
from transformers import AutoTokenizer, TFBertForSequenceClassification
# Initialize the tokenizer with the pretrained Pygmalion 7B model from Hugging Face's model hub
tokenizer = AutoTokenizer.from_pretrained("facebook/pygmalion-large")
# Initialize the model with the pretrained Metharme 7B model from Hugging Face's model hub
model = TFBertForSequenceClassification.from_pretrained("facebook/metharme-7b")
# Move the model to the appropriate device (e.g. GPU)
model.to(device)
Now, let’s say you want to generate a response based on the input text “What is your favorite color?” Here’s how you can do it:
# Importing necessary libraries
import tensorflow as tf
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Defining input text
input_text = "What is your favorite color?"
# Initializing tokenizer with T5 model
tokenizer = T5Tokenizer.from_pretrained('t5-base')
# Encoding input text using tokenizer
encoded_input = tokenizer(input_text, return_tensors="tf")
# Initializing T5 model for generation
model = T5ForConditionalGeneration.from_pretrained('t5-base')
# Generating response using T5 model
outputs = model.generate(**encoded_input)
# Decoding output using tokenizer and removing special tokens
decoded_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# Printing the response
print("Bot: ", decoded_output)
# The code above takes the input text, encodes it using the T5 tokenizer, and then uses the T5 model to generate a response. The response is then decoded using the tokenizer and printed as the output. The T5 model is a transformer-based model that is trained for various natural language processing tasks, including text generation. The tokenizer is used to tokenize the input text and the model is used to generate a response based on the encoded input. The output is then decoded to remove any special tokens and printed as the final response from the bot.
And that’s it! You can customize the output by changing the input text or using different models (like Pygmalion 7B for shorter responses and Metharme 7B for longer ones). So, go ahead and let your creativity run wild with these amazing tools!