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Jimmy McBride
Jimmy McBride Subscriber

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πŸš€ Supercharge Your GPT-3 Model with Personalized Training: A Quick & Easy Guide! 🌟

Unlock the true potential of GPT-3 by training your own personalized model! By customizing your model with domain-specific data, you can create a powerful AI assistant tailored to your unique needs. Imagine having your own AI-powered blog writer that mimics your writing style, making it easier to maintain a consistent online presence. Or how about an AI that understands the intricacies of your codebase and helps you debug and optimize your code? Solo entrepreneurs can benefit immensely from a customized GPT-3 model that's trained to assist with tasks like customer support, personalized outreach, drafting proposals, and even generating marketing ideas. In short, training your own GPT-3 model opens up a world of possibilities and empowers you to supercharge your productivity, creativity, and efficiency! πŸš€

Step 1: Install the OpenAI CLI

First, you need to install the OpenAI CLI on your machine. Open your terminal and run the following command:

pip install openai
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Step 2: Set up your API key

Export your OpenAI API key as an environment variable:

export OPENAI_API_KEY=<your_api_key>
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Step 3: Prepare your training data

Create a JSONL file containing your training examples. In this case, we're using blog-related prompts:

{"prompt": "What is Midjounrey?", "completion": "<an explanation of what Midjourney is>"}
{"prompt": "How does Midjounrey work?", "completion": "<an explanation of how Midjourney works>"}
{"prompt": "What are all of Midjounrey's parameters and what do they do?", "completion": "<a list of all parameters Midjourney has and explanations for each>"}
{"prompt": "Give me a list of amazing Midjounrey's prompts by the community?", "completion": "<a list of all your favorite Midjourney prompts>"}
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Save this file as train_data.jsonl.

Step 4: Train your custom GPT-3 model with the Davinci model

Now, use the OpenAI CLI to train your custom GPT-3 model with the Davinci model as the base model. Replace <path_to_train_data.jsonl> with the path to your train_data.jsonl file.

openai api fine_tunes.create -m davinci --n_epochs 3 \
    -t <path_to_train_data.jsonl>
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Step 5: Training the model with additional data

If you want to train your model with more data, simply add new examples to your train_data.jsonl file and run the fine-tuning command again. The updated model will be trained on the new data.

Step 6: Use your custom model

Once your model is trained, you can use it with the OpenAI API. Replace <model_ID> with the ID of your custom model:

openai api completions.create -m <model_ID> \
  --max-tokens 30 --temperature 0 --stop "###" \
  -p "Your prompt here"
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And that's it! πŸŽ‰ You've successfully trained a custom GPT-3 model using the OpenAI CLI, and you can now use it to generate more accurate and personalized results. Enjoy your supercharged GPT-3 model! Don't forget to follow for more exciting tips and tricks! 😊

Playwright CLI Flags Tutorial

5 Playwright CLI Flags That Will Transform Your Testing Workflow

  • 0:56 --last-failed: Zero in on just the tests that failed in your previous run
  • 2:34 --only-changed: Test only the spec files you've modified in git
  • 4:27 --repeat-each: Run tests multiple times to catch flaky behavior before it reaches production
  • 5:15 --forbid-only: Prevent accidental test.only commits from breaking your CI pipeline
  • 5:51 --ui --headed --workers 1: Debug visually with browser windows and sequential test execution

Learn how these powerful command-line options can save you time, strengthen your test suite, and streamline your Playwright testing experience. Click on any timestamp above to jump directly to that section in the tutorial!

Watch Full Video πŸ“ΉοΈ

Top comments (1)

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leob profile image
leob β€’

That's excellent, thanks ... I didn't expect that it would be so simple to extend GPT-3. Do you know if we can expect more or less the same for GPT-4 ?

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