Instructions to use NhatDFO/sf_blip2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NhatDFO/sf_blip2 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="NhatDFO/sf_blip2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NhatDFO/sf_blip2") model = AutoModelForMultimodalLM.from_pretrained("NhatDFO/sf_blip2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from NhatDFO/sf_blip2: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/NhatDFO/sf_blip2/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
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hf download hf://NhatDFO/sf_blip2@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/NhatDFO/sf_blip2/resolve/refs%2Fpr%2F1/tokenizer.json
2.11 MB
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