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[Music] today were hosting meetup with todor markov who is a researcher at openai and hes working currently on openai api where hes focusing on safety and monitoring the api and todder will share with us about how they use api to fine-tune gpt3 models and there will be some exciting live demos as well so todder thanks a lot for joining and its were very much looking forward to learning from you today yep thank you sophia for the kind introduction uh hi all as sophia mentioned today i will be talking to you about the open ai fine tuning api this is a new api which we released a bit over a month ago it allows you to fine-tune our gpt-3 family of language models on your own data creating specialized models with much higher performance on their task domain in this talk i will give a broad overview of the ways in which fine tuning can help your concrete use case i will then run through a live demo of how the fine tuning api works and im going to talk a little bit about the challenges

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Why do we do fine-tuning?

Applying fine-tuning allows us to utilize pre-trained networks to recognize classes they were not originally trained on. And furthermore, this method can lead to higher accuracy than transfer learning via feature extraction.

How much does it cost to fine-tune GPT-3?

The more expensive models, such as the Da Vinci model, will cost more to fine-tune than the cheaper models, such as the Curie model. Assuming you use a data set of 200,000 examples and training iterations of 10, the cost to fine-tune GPT-3 would be about $200.

How does model fine-tuning work?

Fine-tuning trains a pretrained model on a new dataset without training from scratch. This process, also known as transfer learning, can produce accurate models with smaller datasets and less training time.

How to fine-tune BERT for classification?

BERT requires the following preprocessing steps: Add special s: - [CLS] : at the beginning of each sentence (ID 101 ) Make sentences of the same length: - This is achieved by padding, i.e. adding values of convenience to shorter sequences to match the desired length. Create an attention mask:

What does fine-tuning process mean?

Fine-tuning is the process investors and investment professionals use to make small modifications or improvements to investment portfolios. It can be executed using different strategies, such as technical analysis, either manually or automatically using new technology.

How can you fine tune the performance of neural network?

Generally, this is done by replacing the last fully-connected layer and training the model while only updating the weights of the linear layers and letting the convolutional layers keep their weights.

How do you do fine-tuning?

Fine-tuning, on the other hand, requires that we not only update the CNN architecture but also re-train it to learn new object classes. Remove the fully connected nodes at the end of the network (i.e., where the actual class label predictions are made). Replace the fully connected nodes with freshly initialized ones.

What is fine-tuning in neural network?

Fine-tuning is a way of applying or utilizing transfer learning. Specifically, fine-tuning is a process that takes a model that has already been trained for one given task and then tunes or tweaks the model to make it perform a second similar task.

Is the OpenAI playground free?

The playground is mostly free but has a time limit When you make your OpenAI account, youre given a credit of $18 to start with. Considering that the AI only costs about six cents per 4000 characters generated, that $18 should last you an incredibly long time. After three months, the free credits will expire.

How many epochs for fine-tuning?

If youre fine-tuning a pre-trained model on a small dataset this can already be the case after 2-4 epochs. On the other hand if you train a very deep neural net from scratch on a large dataset you will need to train for dozens of epochs until your model fits the data well.

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