Step-by-step guide for GPT-3 fine-tuning
Since the end of 2022, the launch of ChatGPT by OpenAI has been considered by many of us to be the iPhone moment of AI. However, OpenAI’s chatbot is not the first generative AI text machine learning model and follows the launch of GPT-3, which was launched two years earlier.
OpenAI provides us with a ready-to-use GPT-3 trained model. Furthermore, a specific task can be fine-tuned on a smaller dataset. For example, suppose you want to create an email response generator specific to your company. First, you must collect a large amount of data about your particular business domain, such as customer email inquiries and responses. You can then use this data to fine-tune GPT-3 to learn your company’s specific language patterns and phrases. By fine-tuning GPT-3, creating a highly customized and specialized email response generator is possible, specifically tailored to the language patterns and words used in a particular business domain.
In this blog post, I will show you how to fine-tune GPT-3. We will do this with python code and without assuming prior knowledge about GPT-3.
What is needed for fine-tuning?
Unlike the GPT-2 model available for instance on Hugging Face (when this blog is written), we don’t have direct access to the GPT-3 model. Therefore, you first need to get an API key from OpenAI and install the Python package openai, which can be quickly done via pip.
For the API key from OpenAI:
- go to https://platform.openai.com/account/api-keys ,
- create an account,
- click on ‘Create new secret key’ and
- do a copy of the key.
The key is a long string of characters starting with ‘sk-’. Make sure you keep it secret! Once you have your key, an easy way to get access to your key is to do the following in your terminal: (personally, for simplicity, I put it in my .bachrc):
export OPENAI_API_KEY=sk-t59pgejhtrff5(...)
Using GPT-3 models has a cost. We need credits. At the time of this writing, when you create a new account, you get free credits to try out the tool. I don’t know if this will continue…
Now that we have our key and Python package, it is time to think about the data we need to fine-tune. First, we need a file of examples for fine-tuning, where each example is a prompt followed by the appropriate completion.
A superhero description generation tool
A superhero from DALL-E 2
We will build a tool for this demo to create descriptions of imaginary superheroes. In the end, the tool will receive the age, gender, and power of the superhero, and it will automatically produce a description of our superhero.
In the following example, after fine-tuning the model, all we have to say is ’40, woman, Healing ->’, and we will automatically get a description from the model.
This is what it’s all about!!
标签：Unleashing,his,prompt,Tuning,power,fine,Superhero,model,data From： https://www.cnblogs.com/silva/p/17134296.html