Visual Question Answering
Transformers
Safetensors
English
llava_llama
text-generation
mol
multimodal
custom-code
Instructions to use wjdghks950/vicuna_clip_patch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wjdghks950/vicuna_clip_patch 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="wjdghks950/vicuna_clip_patch")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("wjdghks950/vicuna_clip_patch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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