- Introduction and Setup
- Course Introduction
- Environment Setup
- Advanced Neural Network Architectures
- Advanced Convolutional Neural Networks (CNNs)
- Implementing ResNet for Image Classification Tasks from Scratch
- Recurrent Neural Networks (RNNs) and LSTMs
- Understanding RNNs
- Code Sample for RNN
- Understanding LSTMs
- Code Sample for LSTM
- Understanding GRUs
- Code Sample for GRU
- Generative Adversarial Networks (GANs)
- Understanding the Architecture of GANs
- The Role of the Generator and Discriminator
- Implementing a Simple GAN for Image Generation
- Advanced GAN Techniques
- Implementing a Conditional GAN for MNIST Digit Generation
- Wasserstein GANs (WGANs)
- Implementing a WGAN for MNIST Digit Generation
- CycleGANs for image translation
- Implementing a CycleGAN for Image Translation
- Variational Autoencoders (VAEs)
- Understanding the Architecture and Functioning of VAEs
- The Concept of Latent Space and Reconstruction
- Practical Coding: Implementing VAEs
- Enhancing the VAE
- Transformer Models
- Architecture of the Transformer model
- The Attention Mechanism and Its Importance
- Practical Coding: Implementing Transformers
- Application: Implementing a Transformer for text generation
- Advanced Applications of Generative Models
- Fine-tuning GPT-3 for Custom Text Generation Tasks
- Enhancing Fine-Tuning with More Training Data
- Implementing neural style transfer
- Using GANs for High-Resolution Image Synthesis
- Music and Audio Generation
- Optimization and Fine-Tuning
- Techniques for Optimizing Model Performance
- Using Grid Search and Random Search for Hyperparameter Tuning
- Transfer Learning with BERT Model
- Real-World Projects
- Generating Realistic Faces with GANs
- Building a Chatbot with Transformer Models
- Best Practices and Advanced Tips
- Model Evaluation and Validation
- Measurements of Quantitative Importance for Text Generation
- Deploying Generative Models
- Future Trends and Research Directions
- Emerging Trends in Generative AI
- Ethical Considerations
- Conclusion
Fine-tuning GPT-3 for Custom Text Generation Tasks
Text Generation and Language Models : Fine-tuning GPT-3 for Custom Text Generation Tasks
GPT-3 is a powerful language model that OpenAI made. Fine-tuning it for jobs that require custom text output can make it work much better in certain situations. We're going to show you how to use OpenAI's API to fine-tune GPT-3.
Step 1: Setup and Import Necessary Libraries
First, we need to get the OpenAI Python package and add the tools we need.
| # Install the OpenAI package !pip install openai # Import the library import openai |
Pip is used to install the OpenAI package.
To work with the GPT-3 model, we bring in the OpenAI package.
Step 2: Set Up API Key
To prove that our requests are real, we need to set up our OpenAI API key. OpenAI will give you an API key.
# Set up the API key
|
Put in your real OpenAI API key instead of "your-api-key." You can get into the GPT-3 type with this key.
Step 3: Prepare the Training Data
The training data needs to be set up in a way that GPT-3 can understand. You should use the JSONL (JSON Lines) style for the data.
| # Example training data training_data = [ {"prompt": "Translate English to French: 'Hello, how are you?'\n\n", "completion": "Bonjour, comment ça va?\n"}, {"prompt": "Translate English to French: 'What is your name?'\n\n", "completion": "Comment t'appelles-tu?\n"} ] # Save the training data to a file with open('training_data.jsonl', 'w') as f: for item in training_data: f.write(f"{json.dumps(item)}\n") |
We make a list of dictionaries, and each one is a teaching case with a question and an answer.
The training data is saved in a JSONL file.
Step 4: Upload Training Data to OpenAI
We send the training data file to OpenAI so that it can be improved.
| # Upload the training data file response = openai.File.create( file=open('training_data.jsonl', 'rb'), purpose='fine-tune' ) training_file_id = response['id'] |
The training data file is uploaded with the openai.File.create method.
The file is given a number that we will later use. This number is stored in the training_file_id variable.
Step 5: Fine-tune the Model
The submitted training data file is used to start the fine-tuning process.
| # Fine-tune the model response = openai.FineTune.create(training_file=training_file_id, model="davinci") fine_tune_id = response['id'] |
We call the openai.FineTune.create method to start the fine-tuning process.
We tell it to use the uploaded file ID as the training_file and "davinci" as the model.
There is an ID given to the fine-tuning job, which we store in the fine_tune_id variable.
Step 6: Check Fine-tuning Status
The fine-tuning process is tracked so we know when it's done.
| # Check fine-tuning status response = openai.FineTune.retrieve(id=fine_tune_id) status = response['status'] print(f"Fine-tuning status: {status}") |
We use the openai.FineTune.retrieve method to get the status of the fine-tuning job.
To keep track of the work, we print the state.
Step 7: Use the Fine-tuned Model
The fine-tuned model can be used to make words once the fine-tuning is done.
| # Generate text using the fine-tuned model response = openai.Completion.create( model=fine_tune_id, prompt="Translate English to French: 'Good morning!'\n\n", max_tokens=50 ) print(response.choices[0].text.strip()) |
We use the finish of openai.fine-tuned model to make a method that makes text.
We give you a prompt and tell you what the model is: the fine-tuned model ID.
The length of the created text is limited by the value we set for max_tokens.
We print the words that we made.
Full Code
| # Install the OpenAI package !pip install openai # Import the library import openai import json # Set up the API key openai.api_key = 'your-api-key' # Example training data training_data = [ {"prompt": "Translate English to French: 'Hello, how are you?'\n\n", "completion": "Bonjour, comment ça va?\n"}, {"prompt": "Translate English to French: 'What is your name?'\n\n", "completion": "Comment t'appelles-tu?\n"} ] # Save the training data to a file with open('training_data.jsonl', 'w') as f: for item in training_data: f.write(f"{json.dumps(item)}\n") # Upload the training data file response = openai.File.create( file=open('training_data.jsonl', 'rb'), purpose='fine-tune' ) training_file_id = response['id'] # Fine-tune the model response = openai.FineTune.create(training_file=training_file_id, model="davinci") fine_tune_id = response['id'] # Check fine-tuning status response = openai.FineTune.retrieve(id=fine_tune_id) status = response['status'] print(f"Fine-tuning status: {status}") # Generate text using the fine-tuned model response = openai.Completion.create( model=fine_tune_id, prompt="Translate English to French: 'Good morning!'\n\n", max_tokens=50 ) print(response.choices[0].text.strip()) |
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