Instructions to use hang1n/t5-large_Prefix_Tuning_seq2seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hang1n/t5-large_Prefix_Tuning_seq2seq with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hang1n/t5-large_Prefix_Tuning_seq2seq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_config.json from hang1n/t5-large_Prefix_Tuning_seq2seq: direct link, hf CLI and curl.
- Browser
- Download file 370 Bytes
-
https://huggingface.co/hang1n/t5-large_Prefix_Tuning_seq2seq/resolve/main/adapter_config.json
- Command line
-
hf download hf://hang1n/t5-large_Prefix_Tuning_seq2seq/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/hang1n/t5-large_Prefix_Tuning_seq2seq/resolve/main/adapter_config.json
370 Bytes
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "t5-large", | |
| "encoder_hidden_size": 1024, | |
| "inference_mode": true, | |
| "num_attention_heads": 16, | |
| "num_layers": 24, | |
| "num_transformer_submodules": 2, | |
| "num_virtual_tokens": 20, | |
| "peft_type": "PREFIX_TUNING", | |
| "prefix_projection": false, | |
| "revision": null, | |
| "task_type": "SEQ_2_SEQ_LM", | |
| "token_dim": 1024 | |
| } |