Instructions to use harsh13333/gemma-clip-vqa_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harsh13333/gemma-clip-vqa_v1 with Transformers:
# Load model directly from transformers import GemmaCLIPVLM model = GemmaCLIPVLM.from_pretrained("harsh13333/gemma-clip-vqa_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from harsh13333/gemma-clip-vqa_v1: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/harsh13333/gemma-clip-vqa_v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://harsh13333/gemma-clip-vqa_v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/harsh13333/gemma-clip-vqa_v1/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 9b1b408d9077d3664876e5eaed030841f90d25b7c4bfbbcf10cd01c5344837d5
- Size of remote file:
- 33.4 MB
- SHA256:
- 3ff2eb2f470517c6123c6224cd75fa4b373953af438afa4b3114c9d7cf3309d8
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