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Description
Lora fine-tuning is an adapter-based technique to fine-tune an LLM. It changes LLM model architecture by adding learnable lora layers to transformers. During fine-tuning, only lora weights are adjustable and the LLM weights are frozen, so it requires much less GPU memory comparing to a full-layer fine-tuning. Based on this table, it requires 16GB memory to fine-tuning a 7B size model in 16bits, which can be fit in rtx 3090, 4080 and 4090. A wider range of GPUs can be fit on 3.8B LLMs like phi-3.5-mini
API design (wip)
Package: Microsoft.ML.GenAI.Lora
interface ICausalLMLoraPipeline {} // pipeline for loading causal LM + lora layers
class LoraConfiguration // lora configuration