Visual Question Answering
Transformers
Safetensors
English
panovlm_decision
feature-extraction
panovlm
decision-model
calibration
fastvit
linear-attention
custom_code
Instructions to use PanocularAI/PanoVLD-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PanocularAI/PanoVLD-500M 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="PanocularAI/PanoVLD-500M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PanocularAI/PanoVLD-500M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from PanocularAI/PanoVLD-500M: direct link, hf CLI and curl.
- Browser
- Download file 221 Bytes
-
https://huggingface.co/PanocularAI/PanoVLD-500M/resolve/main/processor_config.json
- Command line
-
hf download hf://PanocularAI/PanoVLD-500M/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/PanocularAI/PanoVLD-500M/resolve/main/processor_config.json
221 Bytes
| { | |
| "processor_class": "PanoVLMDecisionProcessor", | |
| "image_seq_length": 144, | |
| "max_len": 1024, | |
| "max_option_tokens": 48, | |
| "auto_map": { | |
| "AutoProcessor": "processing_panovlm_decision.PanoVLMDecisionProcessor" | |
| } | |
| } |