Instructions to use RobinWZQ/backdoor_cat2dog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobinWZQ/backdoor_cat2dog with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RobinWZQ/backdoor_cat2dog", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download text_encoder/config.json from RobinWZQ/backdoor_cat2dog: direct link, hf CLI and curl.
- Browser
- Download file 628 Bytes
-
https://huggingface.co/RobinWZQ/backdoor_cat2dog/resolve/main/text_encoder/config.json
- Command line
-
hf download hf://RobinWZQ/backdoor_cat2dog/text_encoder/config.json
-
curl -L -o config.json https://huggingface.co/RobinWZQ/backdoor_cat2dog/resolve/main/text_encoder/config.json
628 Bytes
| { | |
| "_name_or_path": "/mnt/wangzhongqi/Models/stable-diffusion-v1-4", | |
| "architectures": [ | |
| "CLIPTextModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 0, | |
| "dropout": 0.0, | |
| "eos_token_id": 2, | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 768, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 77, | |
| "model_type": "clip_text_model", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "projection_dim": 512, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.2", | |
| "vocab_size": 49409 | |
| } | |