This submit is a technical submit summarizing my expertise with the Ray library for distributed information processing and showcasing an instance of utilizing Ray for scalable offline batch inference.
Just lately, I needed to put together a dataset for Imaginative and prescient LLM coaching. The standard of the coaching dataset is important for the success of the coaching and we would have liked to develop instruments for processing giant quantities of information. The objective is to verify the information feeding the mannequin is managed and top quality.
Why a lot effort to create a dataset? Isn’t amount the key of LLM?
It’s not. First, Let me share why engineering effort ought to be given to setting up and filtering an excellent dataset.
Within the present race for the event of basis fashions, many new fashions emerge each month on the high of the SOTA benchmarks. Some firms or laboratories share the weights with the open-source group. They generally even share checkpoints and coaching scripts.
Nevertheless, the steps of creation and curation of the coaching datasets are hardly ever shared. For…
