Fine-tuning open AI models using Hugging Face TRL

Fine-tuning pre-trained language models using supervised fine-tuning can enhance their performance on specific tasks, like code generation or medical text summarization. The process involves preparing a high-quality labeled dataset, leveraging tools like Hugging Face TRL, and deploying it on a platform like Google Kubernetes Engine. Fine-tuning can be achieved with Parameter-Efficient Fine-Tuning (PEFT) to reduce memory requirements and produce a smaller, specialized model.

Fine-tuning Process

• 00:00:09 Fine-tuning involves training a pre-trained language model on a dataset of inputs and desired outputs to enhance its performance on specific tasks, such as sentiment analysis, code generation, or text summarization. Fine-tuning can result in a smaller model outperforming larger general-purpose models on specific tasks. Before fine-tuning, it is recommended to evaluate the model, experiment with prompt engineering, and ensure the availability of a high-quality labeled dataset.

Fine-tuning with TRL

• 00:01:42 Hugging Face TRL is an open-source library that simplifies the fine-tuning process using adapters, which are small sets of specialized weights added to a base model. The speaker demonstrates fine-tuning Google's Gemma 2 model using TRL and PEFT on Google Kubernetes Engine, leveraging a Cloud Storage bucket and an NVIDIA L4 GPU for training. The fine-tuned model outputs a small adapter file containing the updated weights, significantly reducing storage needs.