Instructions to use hf-internal-testing/tiny-random-MistralModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MistralModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MistralModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MistralModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46dc70a2fe13931048d4924b993ab73d7cdd1a785c430992272b6d25c61c8408
- Size of remote file:
- 4.16 MB
- SHA256:
- 321410b9a3e3a5bdd742ce9fda467c1b58c08384f037347de1280e6037a41e28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.