Instructions to use hf-tiny-model-private/tiny-random-TvltModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-TvltModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-TvltModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-TvltModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-TvltModel: direct link, hf CLI and curl.
- Browser
- Download file 5 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-TvltModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-TvltModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-TvltModel/resolve/main/pytorch_model.bin
5 MB
- Xet hash:
- acd7f3f7f4accea75eebda7b09a8afd364e18b4bb4354decf8a141939d3fe7bb
- Size of remote file:
- 5 MB
- SHA256:
- ab7aec4d6519e5cccf8789eb9e3530cb6f115ace1c6b8bea1cb0c3d5d91b722c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.