Instructions to use hf-internal-testing/tiny-random-MptModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MptModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MptModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MptModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MptModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 933ceb79fdc5499f6bc04f93f8309d0292b9087b88334764aa7b629257e70774
- Size of remote file:
- 388 kB
- SHA256:
- b03928d0d7caa238bdb8d7b2c9643f026d30e02238541d815dbab6670388c6ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.