Instructions to use tr-aravindan/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tr-aravindan/output with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gpt2") model = PeftModel.from_pretrained(base_model, "tr-aravindan/output") - Notebooks
- Google Colab
- Kaggle
File size: 361 Bytes
518f484 4ed0561 518f484 4ed0561 518f484 4ed0561 518f484 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"auto_mapping": null,
"base_model_name_or_path": "gpt2",
"encoder_hidden_size": 768,
"inference_mode": true,
"num_attention_heads": 12,
"num_layers": 12,
"num_transformer_submodules": 1,
"num_virtual_tokens": 20,
"peft_type": "PREFIX_TUNING",
"prefix_projection": false,
"revision": null,
"task_type": "CAUSAL_LM",
"token_dim": 768
} |