Instructions to use asier86/mi_modelo_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asier86/mi_modelo_small with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="asier86/mi_modelo_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("asier86/mi_modelo_small") model = AutoModelForQuestionAnswering.from_pretrained("asier86/mi_modelo_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from asier86/mi_modelo_small: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/asier86/mi_modelo_small/resolve/main/training_args.bin
- Command line
-
hf download hf://asier86/mi_modelo_small/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/asier86/mi_modelo_small/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 50a9f3bf5acddc91b9a0280062f3427bb465e763bfe18bea7a85e7528fa62bb7
- Size of remote file:
- 4.54 kB
- SHA256:
- 40af413b85d1676d0f5cd4342b4f783656edec6a435d2f9337a94b3bf7240ddb
路
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