Instructions to use intelcomp/ipc_level1_F with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/ipc_level1_F with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/ipc_level1_F")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/ipc_level1_F") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/ipc_level1_F", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from intelcomp/ipc_level1_F: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/intelcomp/ipc_level1_F/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://intelcomp/ipc_level1_F@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/intelcomp/ipc_level1_F/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.42 GB
- Xet hash:
- 9d5803ed500a896cfa53bbe11a735af0c85c24723af1c90f37f790ca1961c73d
- Size of remote file:
- 1.42 GB
- SHA256:
- d310d7bc7dd5359978436660ff9e735faa514fce78bbd55b73622aa8f2ae41b5
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