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 training_args.bin from dflcmu/fine_tune_spatial: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/dflcmu/fine_tune_spatial/resolve/main/training_args.bin
- Command line
-
hf download hf://dflcmu/fine_tune_spatial/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dflcmu/fine_tune_spatial/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 8143530e745766d8a48fd244fb36767bcdfc5b8e05f7d7daa6c5be725654a28f
- Size of remote file:
- 3.58 kB
- SHA256:
- 3fdd4a4da9a4e0d992e798f658751d73619294effce22d1ff3051a3da0aa89d1
路
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