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🤗 pets
Parameter-Efficient Tuning at Scale with 🤗 Accelerate
Supported methods:
- Prefix Tuning
- P-Tuning
- Prompt Tuning
- LoRA [in backlog]
Models support matrix
Sequence Classification
| Prefix Tuning | P-Tuning | Prompt Tuning | LoRA | |
|---|---|---|---|---|
| BERT | ✅ | ✅ | ✅ | |
| RoBERTa | ✅ | ✅ | ✅ | |
| GPT-2 | ✅ | ✅ | ✅ | |
| Bloom | ✅ | ✅ | ✅ | |
| OPT | ✅ | ✅ | ✅ | |
| GPT-Neo | ✅ | ✅ | ✅ | |
| GPT-J | ✅ | ✅ | ✅ | |
| Deberta | ||||
| Deberta-v2 |
Causal Language Modeling
| Prefix Tuning | P-Tuning | Prompt Tuning | LoRA | |
|---|---|---|---|---|
| GPT-2 | ✅ | ✅ | ✅ | |
| Bloom | ✅ | ✅ | ✅ | |
| OPT | ✅ | ✅ | ✅ | |
| GPT-Neo | ✅ | ✅ | ✅ | |
| GPT-J | ✅ | ✅ | ✅ |
Conditional Generation
| Prefix Tuning | P-Tuning | Prompt Tuning | LoRA | |
|---|---|---|---|---|
| T5 | ✅ | ✅ | ✅ | |
| BART | ✅ | ✅ | ✅ |
Languages
Python
99.8%
Makefile
0.2%