Instructions to use cogniai-dev/bloomz-560-m-peft-method with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cogniai-dev/bloomz-560-m-peft-method with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cogniai-dev/bloomz-560-m-peft-method", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "bigscience/bloomz-560m", | |
| "encoder_dropout": 0.0, | |
| "encoder_hidden_size": 128, | |
| "encoder_num_layers": 2, | |
| "encoder_reparameterization_type": "MLP", | |
| "inference_mode": true, | |
| "modules_to_save": null, | |
| "num_attention_heads": 16, | |
| "num_layers": 24, | |
| "num_transformer_submodules": 1, | |
| "num_virtual_tokens": 20, | |
| "peft_type": "P_TUNING", | |
| "peft_version": "0.19.1", | |
| "revision": null, | |
| "task_type": "CAUSAL_LM", | |
| "token_dim": 1024 | |
| } |