Instructions to use iamthe66epitaph/BabyAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamthe66epitaph/BabyAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="iamthe66epitaph/BabyAI")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iamthe66epitaph/BabyAI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the BSD-style license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| from contextlib import contextmanager | |
| from torch._C._functorch import _vmap_add_layers, _vmap_remove_layers | |
| _enabled = False | |
| def _enable_layers(dims): | |
| global _enabled | |
| assert not _enabled | |
| input = sorted((d._level, d.size) for d in dims if not isinstance(d, int)) | |
| n = len(input) | |
| try: | |
| _vmap_add_layers(input) | |
| _enabled = True | |
| yield | |
| finally: | |
| _enabled = False | |
| _vmap_remove_layers(n) | |