Instructions to use dflcmu/fine_tune_spatial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/fine_tune_spatial 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="dflcmu/fine_tune_spatial")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dflcmu/fine_tune_spatial") model = AutoModelForQuestionAnswering.from_pretrained("dflcmu/fine_tune_spatial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dflcmu/fine_tune_spatial: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/dflcmu/fine_tune_spatial/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dflcmu/fine_tune_spatial/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dflcmu/fine_tune_spatial/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 4d1b87f880e81053295a470dc08263e8bc538b70c873a95eb27343f3ae5e4efc
- Size of remote file:
- 496 MB
- SHA256:
- fdf98fc0a7487c4daa7b360249739742a396946192ac312c4a3ddbb7f5195319
路
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