Instructions to use hpcai-tech/vqvae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hpcai-tech/vqvae with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hpcai-tech/vqvae", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hpcai-tech/vqvae", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "VQVAE" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_vqvae.VQVAEConfig", | |
| "AutoModel": "modeling_vqvae.VQVAE" | |
| }, | |
| "downsample": [ | |
| 2, | |
| 4, | |
| 4 | |
| ], | |
| "embedding_dim": 256, | |
| "model_type": "VQVAE", | |
| "n_codes": 2048, | |
| "n_hiddens": 240, | |
| "n_res_layers": 4, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.2" | |
| } | |