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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-MistralModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 451025a1c6ce105265bdc1f78ddc49b75a49de62621c767de1e2d307fb96045f
- Size of remote file:
- 4.16 MB
- SHA256:
- ba82a13892ed6189e58ec2059c5cfc219287581655c65658d5950e929bcbf56c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.