Scalable personalization of large deep learning models
Cost-efficiently and quickly benefit from progress in large pretrained models by compressing our proprietary multimodal model—optimized for performance on common downstream tasks—to be your AI products.
import reka
reka.set_key(‘YOUR_API_KEY’)
qa_dataset = reka.add_dataset(
name=”multiqa_dataset”, content=multiqa_data,
description=”Multiple choice question answering dataset”)
finetune_job = qa_dataset.finetune(new_model_name=”finetuned_qa_model”)
qa_generate_job = qa_dataset.generate(model=”finetuned_qa_model”
num=10000, new_name=”generated_multiqa_data”)
unlabeled_qa_dataset = reka.add_dataset(
name=”unlabeled_multiqa_dataset”, content=unlabeled_multiqa_data,
description=”A dataset of multiple choice questions”)
annotate_job = unlabeled_qa_dataset.annotate(
new_name=”reka_annotated_unlabeled_qa_dataset”)
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About Us

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