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
Download model.safetensors from hf-internal-testing/tiny-random-MistralModel: direct link, hf CLI and curl.
- Browser
- Download file 4.15 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MistralModel/resolve/refs%2Fpr%2F13/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MistralModel@refs/pr/13/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-MistralModel/resolve/refs%2Fpr%2F13/model.safetensors
4.15 MB
- Xet hash:
- aa7662ea034df0979a51af02ba203b8951e41b7699481f95a10cdacdb21c2547
- Size of remote file:
- 4.15 MB
- SHA256:
- 7fcba5dae760a799272e57400561f21291e68fac9018723d74efeeaec28968a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.