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
Download tokenizer.model from wjdghks950/vicuna_clip_patch: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/wjdghks950/vicuna_clip_patch/resolve/main/tokenizer.model
- Command line
-
hf download hf://wjdghks950/vicuna_clip_patch/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/wjdghks950/vicuna_clip_patch/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 409b63d0f14ab5da7909ddcb93f85878ef0e4f19bfa91194b68ac5b45a5b6e87
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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