peft/README.md

🤗 PET

Parameter-Efficient Tuning. Intergrated with 🤗 Accelerate to scale seamlessly to large models using PyTorch FSDP.

Supported methods:

1. LoRA
2. Prefix Tuning
3. P-Tuning
4. Prompt Tuning

Models support matrix

Sequence Classification

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
BERT	✅	✅	✅	✅
RoBERTa	✅	✅	✅	✅
GPT-2	✅	✅	✅	✅
Bloom	✅	✅	✅	✅
OPT	✅	✅	✅	✅
GPT-Neo	✅	✅	✅	✅
GPT-J	✅	✅	✅	✅
Deberta	✅
Deberta-v2	✅

Causal Language Modeling

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
GPT-2	✅	✅	✅	✅
Bloom	✅	✅	✅	✅
OPT	✅	✅	✅	✅
GPT-Neo	✅	✅	✅	✅
GPT-J	✅	✅	✅	✅

Conditional Generation

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
T5	✅	✅	✅	✅
BART	✅	✅	✅	✅

Caveats:

1. Doesn't work currently with DeeSpeed ZeRO Stage-3. Extending support with DeeSpeed ZeRO Stage-3 is in backlog.