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-Colqwen3.5-2b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataScience-UIBK/Argus-Colqwen3.5-2b-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DataScience-UIBK/Argus-Colqwen3.5-2b-v0", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScience-UIBK/Argus-Colqwen3.5-2b-v0", trust_remote_code=True, dtype="auto") - ColPali
How to use DataScience-UIBK/Argus-Colqwen3.5-2b-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
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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