Instructions to use dusersad12/SentinelLM-EvalRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/SentinelLM-EvalRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dusersad12/SentinelLM-EvalRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dusersad12/SentinelLM-EvalRepo") model = AutoModel.from_pretrained("dusersad12/SentinelLM-EvalRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dusersad12/SentinelLM-EvalRepo: direct link, hf CLI and curl.
- Browser
- Download file 172 Bytes
-
https://huggingface.co/dusersad12/SentinelLM-EvalRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dusersad12/SentinelLM-EvalRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dusersad12/SentinelLM-EvalRepo/resolve/main/pytorch_model.bin
172 Bytes
- Xet hash:
- 390c829f284f049c922be9066b26f801a8f40df263842bcfe214b95676526eb1
- Size of remote file:
- 172 Bytes
- SHA256:
- 34bd7b58f12ab6a297adc6d225cfcb20bbea4838c89ec0cf2a38ed769fe6c113
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