Visual Question Answering
Transformers
Safetensors
cvrr_merged
feature-extraction
cvrr
custom_code
latent-reasoning
Instructions to use dmis-lab/InternVL3-38B-CVRR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmis-lab/InternVL3-38B-CVRR 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="dmis-lab/InternVL3-38B-CVRR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dmis-lab/InternVL3-38B-CVRR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download native_backbone/preprocessor_config.json from dmis-lab/InternVL3-38B-CVRR: direct link, hf CLI and curl.
- Browser
- Download file 287 Bytes
-
https://huggingface.co/dmis-lab/InternVL3-38B-CVRR/resolve/main/native_backbone/preprocessor_config.json
- Command line
-
hf download hf://dmis-lab/InternVL3-38B-CVRR/native_backbone/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/dmis-lab/InternVL3-38B-CVRR/resolve/main/native_backbone/preprocessor_config.json
287 Bytes
| { | |
| "crop_size": 448, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 3, | |
| "size": 448 | |
| } | |