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2022-11-29 18:12:34 +05:30

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# 🤗 pets
Parameter-Efficient Tuning at Scale with 🤗 Accelerate
Supported methods:
1. Prefix Tuning
2. P-Tuning
3. Prompt Tuning
4. 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 | ✅ | ✅ | ✅ | |