Instructions to use llm-jp/Jagle-VL-2.2B-FineVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-jp/Jagle-VL-2.2B-FineVision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="llm-jp/Jagle-VL-2.2B-FineVision", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llm-jp/Jagle-VL-2.2B-FineVision", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from llm-jp/Jagle-VL-2.2B-FineVision: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/llm-jp/Jagle-VL-2.2B-FineVision/resolve/main/tokenizer.json
- Command line
-
hf download hf://llm-jp/Jagle-VL-2.2B-FineVision/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/llm-jp/Jagle-VL-2.2B-FineVision/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 94c6c044ded135612bbbc607652ccf8e71d0a30556e7de27dae03bfe24a1f6c2
- Size of remote file:
- 11.4 MB
- SHA256:
- 22da432194eb2ecf833baadba2ead2291f235451562c53296583fd4122a1eff7
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