Instructions to use dortucx/gpt2-nm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dortucx/gpt2-nm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dortucx/gpt2-nm")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dortucx/gpt2-nm") model = AutoModel.from_pretrained("dortucx/gpt2-nm") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_cfg": { | |
| "embed_dim": 1024, | |
| "vision_cfg": { | |
| "image_size": 224, | |
| "layers": 40, | |
| "width": 1408, | |
| "head_width": 88, | |
| "mlp_ratio": 4.3637, | |
| "patch_size": 14 | |
| }, | |
| "text_cfg": { | |
| "context_length": 77, | |
| "vocab_size": 49408, | |
| "width": 1024, | |
| "heads": 16, | |
| "layers": 24 | |
| } | |
| }, | |
| "preprocess_cfg": { | |
| "mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ] | |
| } | |
| } |