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Full fine-tuning is an alternative to PEFT From the official documentation: “Instead of adjusting all of a model's billions of parameters (full fine-tuning), use parameter-efficient fine-tuning (PEFT) methods like low-rank adaptation (LoRA).”
low-rank adaptation is part of PEFT From the official documentation: “Instead of adjusting all of a model's billions of parameters (full fine-tuning), use parameter-efficient fine-tuning (PEFT) methods like low-rank adaptation (LoRA).”
Full fine-tuning is an alternative to low-rank adaptation From the official documentation: “Low-Rank Adaptation (LoRA): LoRA is a [parameter-efficient tuning](/gemini-enterprise-agent-platform/models/tuning#tuning-approaches) mode that only adjust subset of parameters. It's more cost efficient and require less training data than full fine-tuning. On the other hand…”
Full fine-tuning is part of supervised fine-tuning From the official documentation: “- **Supervised fine-tuning:** - [Full fine-tuning](/gemini-enterprise-agent-platform/models/tuning#tuning-approaches) - Low-Rank Adaptation (LoRA): LoRA is a [parameter-efficient tuning](/gemini-enterprise-agent-platform/models/tuning#tuning-approaches) mode that only adjust…”
low-rank adaptation is part of supervised fine-tuning From the official documentation: “- **Supervised fine-tuning:** - [Full fine-tuning](/gemini-enterprise-agent-platform/models/tuning#tuning-approaches) - Low-Rank Adaptation (LoRA): LoRA is a [parameter-efficient tuning](/gemini-enterprise-agent-platform/models/tuning#tuning-approaches) mode that only adjust…”