Scalable LLM Fine-Tuning Platform
Design a system that enables users to perform domain-specific fine-tuning of Large Language Models (LLMs) at scale. The system must support dataset ingestion, asynchronous job orchestration on GPU clusters, real-time training progress monitoring, and secure storage of model checkpoints. Consider constraints such as GPU resource scarcity, large file transfer overheads, and the need for fault-tolerant long-running tasks (e.g., handling spot instance preemption).
PyTorchKubernetesS3PostgreSQLLoRAPEFTDockerRabbitMQDeepSpeed
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