Instructions to use cmarkea/distilcamembert-base-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmarkea/distilcamembert-base-qa 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="cmarkea/distilcamembert-base-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("cmarkea/distilcamembert-base-qa") model = AutoModelForQuestionAnswering.from_pretrained("cmarkea/distilcamembert-base-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cmarkea/distilcamembert-base-qa: direct link, hf CLI and curl.
- Browser
- Download file 270 MB
-
https://huggingface.co/cmarkea/distilcamembert-base-qa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cmarkea/distilcamembert-base-qa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cmarkea/distilcamembert-base-qa/resolve/main/pytorch_model.bin
270 MB
- Xet hash:
- 2604c72e414d470870ab318bc73ae60cc3e7bf48e41c6dc73c997b5d4d1f095f
- Size of remote file:
- 270 MB
- SHA256:
- 454014ecbe40213dc05c300650c9fbfd04bc31de8ebb4c3c55473534ff8abd44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.