Fine-tuning ChatGPT with OpenAI Tutorial - [Customize a model for your application in 12 Minutes]

Corbin Brown
Corbin Brown
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Publicado 2024-03-05
Let's learn about fine-tuning OpenAI's models, such as ChatGPT, enhances their performance by training on a specific dataset tailored to your needs. This process yields higher quality outputs, requires fewer tokens for prompts, and decreases response times by reducing the need for extensive examples within prompts. It involves preparing a custom dataset, training a new model based on this data, evaluating its effectiveness, and iteratively improving until satisfactory results are achieved, ultimately allowing for more efficient and tailored use of the model in various applications.
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Fine-tuning OpenAI Documentation
Learn how to customize a model for your application.
platform.openai.com/docs/guides/fine-tuning/fine-t…

Data preparation and analysis for chat model fine-tuning
cookbook.openai.com/examples/chat_finetuning_data_…

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Key Takeaways:

✩ Enhanced Model Performance: Fine-tuning improves the model's ability to generate high-quality results specific to your application, outperforming standard few-shot learning techniques.
✩ Efficiency and Cost Savings: By reducing the need for lengthy prompts, fine-tuning leads to token savings and faster response times, making the model more economical and efficient to use.
✩ Iterative Improvement Process: The fine-tuning process is iterative, involving preparation of training data, model training, and evaluation, allowing for continuous improvement of the model's performance.

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Todos los comentarios (21)
  • @Mnkeys
    You're a LEGEND. Can't tell you how helpful this was
  • @paulwahtel7200
    Awesome video! I would like to see more content of how you can improve the model and make it even more powerful for clients)) It was awesome, if you could use then fine tuned model via another interfaces such as Zapier!
  • @webcafeai
    Navigate to key moments👇 made via tubestamp.com/ 0:03 - Understanding the concept of fine-tuning models 1:44 - Importance of providing example inputs and outputs for fine-tuning 4:09 - Importance of having 10 different data points for fine-tuning 6:30 - Minimum of 10 example data points required for training 9:46 - Training cost estimation and model success 11:32 - Leveraging fine-tuned models in OpenAI playgroun
  • @aritra80
    Thank you so much for this video! It was so helpful
  • @Wladimirko
    Very helpful and valuable content. thank you corbin!
  • @strategy419
    more of fine tuning cideo like this please
  • @douglas_dev1
    Great video! can you do one showing how to custom an assistant and calling it over api? ^^
  • @axelmontois3676
    Amazing video. There is jsut something that I don't understand. What to put in content in the fine-tuning file.
  • @Alex-dk1um
    Thank you for all the content so far. I work in a smaller company and want to be the go-to person for AI and specifically the section of AI you do your videos on. What would you suggest working on first? You should do some lessons even if those 'courses' are behind a pay wall, I want to learn everything.
  • @manwithnocode
    Can i do it with like input size of 3000 tokens and output size of 1000 tokens or so?
  • @zainulkhan8381
    What changes will be in the the model link if suffix is null I have not defined any kind of suffix and when I hit the completion api I get 404
  • @ese02
    Amazing tutorial. Thanks for sharing. PS: what kinda app/soft u use to produce ur videos (green screen ?)
  • @AlexandraSchoenberg
    Hi Corbin!! Your videos are so helpful. I would love a tutorial on integrating these fine-tuned OpenAI models easily into an Action in a custom GPT. I have been stuggling with getting the ChatGPT interface to always send inputs (in JSON format) to my specific fine-tuned model and output the results (also should be in JSON format). The model, which is actually a GPT-4 fine-tune, works seamlessly in the Playground though. Just wondering if you've tried this or plan to.