Instructions to use intelcomp/ipc_level1_A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/ipc_level1_A with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/ipc_level1_A")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/ipc_level1_A") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/ipc_level1_A", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from intelcomp/ipc_level1_A: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/intelcomp/ipc_level1_A/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://intelcomp/ipc_level1_A/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/intelcomp/ipc_level1_A/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 23a22e8a615d6913f26e4793177704447a35a6943f77af6b7dac88863e053c0a
- Size of remote file:
- 1.42 GB
- SHA256:
- 38b922152043be2d9c8c0cdd6123a06fb3e3d564f2c455337d652e3dd761ca5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.