Fine-tune Line Work For Free with DocHub and make the most of your documents

Aug 6th, 2022
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How to Fine-tune Line Work For Free

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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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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.
GPT-3 has many limitationsreliability, interpretability, accessibility, speed, and morethat constrain its capabilities. While these limitations may be addressed in future iterations of GPT, none are trivialand some are very challengingto fix.
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.
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.
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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.
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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.

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