Feature Extraction
Transformers
Safetensors
ColPali
English
argus_colqwen35
visual-document-retrieval
colqwen
text
image
multimodal-embedding
vidore
mixture-of-experts
late-interaction
query-conditioned-routing
custom_code
Instructions to use DataScience-UIBK/Argus-Colqwen3.5-9b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataScience-UIBK/Argus-Colqwen3.5-9b-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DataScience-UIBK/Argus-Colqwen3.5-9b-v0", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScience-UIBK/Argus-Colqwen3.5-9b-v0", trust_remote_code=True, dtype="auto") - ColPali
How to use DataScience-UIBK/Argus-Colqwen3.5-9b-v0 with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Ctrl+K
Add comprehensive model card with per-task ViDoRe v1+v2 numbers (V1=0.9267, V2=0.6915)
15acc90 verified