Instructions to use hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration 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="hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 899 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-Blip2ForConditionalGeneration/resolve/refs%2Fpr%2F1/model.safetensors
899 kB
- Xet hash:
- 879172818e07711072be1734ed76702ac466458000510c64a9b0c6a7e1c9cae7
- Size of remote file:
- 899 kB
- SHA256:
- 739d86c91e81049d6340f4a1aef11eb8648af0175bb45b9e5807a239d1ebead7
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