Instructions to use phucd/blip-gqa-ft2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phucd/blip-gqa-ft2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-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("visual-question-answering", model="phucd/blip-gqa-ft2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("phucd/blip-gqa-ft2") model = AutoModelForMultimodalLM.from_pretrained("phucd/blip-gqa-ft2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from phucd/blip-gqa-ft2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/phucd/blip-gqa-ft2/resolve/main/training_args.bin
- Command line
-
hf download hf://phucd/blip-gqa-ft2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/phucd/blip-gqa-ft2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 24010f5ab657b43f2b8e886171092a729a0a6345c08518fdb15b4faf6459f8d7
- Size of remote file:
- 5.3 kB
- SHA256:
- 531368d863c46c9cc9a4e18bd74e3e0424c0b1cc604e2cc1e8b0b3dcc45f3f0a
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