Instructions to use YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 1.44 GB
-
https://huggingface.co/YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Apache_5_GPT2_Microsoft_Under_Sampling/resolve/main/pytorch_model.bin
1.44 GB
- Xet hash:
- 2c3803b003c62b167e7968eebbc8bf253ca971856d0ff9dfe100815d7118a5c9
- Size of remote file:
- 1.44 GB
- SHA256:
- 46f7700577259c5d61cbd6d96eeb8ba779f08f57ea031901c42fab0d205c50f7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.