[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-docvqa":3},{"tag":4,"articles":10},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":9},"b856003a-110d-4555-aeb2-ed5c510ebce7","DocVQA","docvqa",2,null,[11],{"id":12,"slug":13,"title":14,"summary":15,"category":16,"image_url":17,"cover_image":17,"language":18,"created_at":19},"4a3e15ba-07e8-4e4d-b5c8-d9a46deea8bd","aws-s3-sagemaker-unified-studio-fine-tuning-en","AWS uses S3 to speed LLM fine-tuning","AWS shows how SageMaker Unified Studio, S3, and MLflow can fine-tune Llama 3.2 11B Vision Instruct on DocVQA data.","model-release","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775139362238-r31j.png","en","2026-04-02T14:15:38.340988+00:00"]