Instructions to use dusersad12/NimbusLM-EvalRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/NimbusLM-EvalRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dusersad12/NimbusLM-EvalRepo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dusersad12/NimbusLM-EvalRepo") model = AutoModel.from_pretrained("dusersad12/NimbusLM-EvalRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dusersad12/NimbusLM-EvalRepo: direct link, hf CLI and curl.
- Browser
- Download file 29 Bytes
-
https://huggingface.co/dusersad12/NimbusLM-EvalRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dusersad12/NimbusLM-EvalRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dusersad12/NimbusLM-EvalRepo/resolve/main/pytorch_model.bin
29 Bytes
- Xet hash:
- 7c2320c7e51af4e49aa81523b7a56a338aedea24e9d1593f09843cbff48e1530
- Size of remote file:
- 29 Bytes
- SHA256:
- 679a0f651a7d8ecd2b4d359ffcdd07351771f0b77f7b3ed7ed6b74f788ae18c9
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