Instructions to use jaimin/Bullet_Point with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaimin/Bullet_Point 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="jaimin/Bullet_Point")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("jaimin/Bullet_Point") model = AutoModelForQuestionAnswering.from_pretrained("jaimin/Bullet_Point", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download rust_model.ot from jaimin/Bullet_Point: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/jaimin/Bullet_Point/resolve/main/rust_model.ot
- Command line
-
hf download hf://jaimin/Bullet_Point/rust_model.ot
-
curl -L -o rust_model.ot https://huggingface.co/jaimin/Bullet_Point/resolve/main/rust_model.ot
499 MB
- Xet hash:
- 5e20dd650bd9dca6aa24e90240792a0d1a8773b1c1c7d6a7e679a0c81081b2e8
- Size of remote file:
- 499 MB
- SHA256:
- 5a16ed126bbc8c4cf794406bac0c7946f62d0f175c02dc54d77a00a6255597ed
路
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