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)# 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 preprocessor_config.json from llm-jp/Jagle-VL-2.2B-FineVision: direct link, hf CLI and curl.
- Browser
- Download file 474 Bytes
-
https://huggingface.co/llm-jp/Jagle-VL-2.2B-FineVision/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://llm-jp/Jagle-VL-2.2B-FineVision/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/llm-jp/Jagle-VL-2.2B-FineVision/resolve/main/preprocessor_config.json
474 Bytes
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "LLMjpVLProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| }, | |
| "auto_map": { | |
| "AutoProcessor": "processing_llmjpvl.LLMjpVLProcessor" | |
| } | |
| } |