How to develop a GPT-2 based application
There are a few different GPT open source models available for free, each with its own strengths and weaknesses. Here are a few of the best options:
When choosing a GPT open source model, it is important to consider your needs and requirements. If you need a model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way, then GPT-2 is a good option. If you need a model that is even larger and more powerful, then GPT-3 is a good option. If you are looking for a model that is still under development but has shown promise, then Bard is a good option.
It is also important to consider the licensing terms of the GPT model that you choose. Some models are available under open source licenses, which means that you can use them for free for any purpose. Other models are available under closed source licenses, which means that you will need to purchase a license in order to use them.
Once you have chosen a GPT open source model, you will need to install it and configure it. The installation process will vary depending on the model that you choose. Once the model is installed, you will need to train it on a dataset of text and code. The training process can take several hours or even days, depending on the size of the dataset and the power of your computer.
Once the model is trained, you can use it to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
This application that uses the GPT-2 open source library/model to create an auto assignment for school kid courses:
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import transformer
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load the GPT-2 model
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
# Get the course name and grade level
course_name = "Math"
grade_level = "3"
num_questions = 10
topics = "addition, subtraction, multiplication, and division"
prompt = f"Write an assignment for the course {course_name} for grade level {grade_level}. The assignment should be {num_questions} questions long and should cover the following topics: {topics}."
# tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# generate the assignments
assignments = model.generate(
input_ids,
attention_mask=input_ids,
pad_token_id=tokenizer.eos_token_id,
max_length=512,
num_return_sequences=1,
temperature=0.7,
top_k=5,
)
# decode and Print the assignment
for assignment in assignments:
assignment = tokenizer.decode(assignment, skip_special_tokens=True)
print(assignment, end="\n")
s
This application will generate an assignment for the specified course and grade level. The assignment will be 10 questions long and will cover the specified topics. The assignment will be generated using the GPT-2 model, which is a large language model that has been trained on a massive dataset of text and code.
Here is an example of an assignment that the application might generate:
1. What is 2 + 2
2. What is 3 - 1?
3. What is 4 * 2?
4. What is 5 / 2?
5. What is the sum of 1, 2, 3, 4, and 5?
6. What is the difference between 10 and 5?
7. What is the product of 2 and 3?
8. What is the quotient of 10 and 2?
9. What is the first odd number greater than 10?
10. What is the first even number less than 10?
This assignment is just an example, and the actual assignment that the application generates will vary depending on the specified course, grade level, and topics.
Thank you.