Fine-Tuning Language Models: CSV Dataset Tutorial with Abhishek Thakur
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In this thrilling tutorial by Abhishek Thakur, he delves into the exhilarating world of fine-tuning large language models like Lama V2 using a dynamic CSV dataset. With columns dedicated to instruction, input, and output, Abhishek showcases the adrenaline-pumping process of converting the data into a format compatible with Auto Train through a custom Python script. The dataset is transformed and saved as train.csv, setting the stage for the heart-pounding action that is about to unfold.
As the installation of Auto Train takes center stage, Abhishek revs up the excitement by detailing the essential setup steps required for this high-octane journey. The Auto Train LLM command is unleashed, initiating the pulse-quickening training process with precision parameters like learning rate and batch size. The race against time begins as the model is put through its paces, promising a nail-biting experience for all involved.
Abhishek's expert guidance doesn't stop there; he offers a glimpse into the future, teasing the possibility of loading the trained model for future use and even hints at the pulse-quickening prospect of pushing the model to the Hugging Face Hub for deployment. With a nod to the daredevils on a single GPU or the free-spirited souls on Google Colab's free version, Abhishek's advice on sharding the dataset adds a thrilling twist to the narrative, ensuring a smooth ride without any memory roadblocks.
In a riveting conclusion, Abhishek revs up the enthusiasm, urging viewers to take the wheel and embark on their own fine-tuning adventures. With an invitation to share feedback, questions, and the promise of more exhilarating content on the horizon, Abhishek's tutorial leaves the audience on the edge of their seats, hungry for more high-speed, high-stakes linguistic exploits.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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