Instructions to use langmatthias/exercise06 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use langmatthias/exercise06 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="langmatthias/exercise06")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("langmatthias/exercise06") model = AutoModelForQuestionAnswering.from_pretrained("langmatthias/exercise06", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-5000/optimizer.pt from langmatthias/exercise06: direct link, hf CLI and curl.
- Browser
- Download file 114 MB
-
https://huggingface.co/langmatthias/exercise06/resolve/main/checkpoint-5000/optimizer.pt
- Command line
-
hf download hf://langmatthias/exercise06/checkpoint-5000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/langmatthias/exercise06/resolve/main/checkpoint-5000/optimizer.pt
114 MB
- Xet hash:
- cadbace84143d9f483b28aa30ea63057e10b98722b1f65cad01b48534e34f6ad
- Size of remote file:
- 114 MB
- SHA256:
- f4b31a9db28d09d87a07eaa9962cc1af9e1538a7aa483e6d67ada5d536a542cc
路
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