Feature Extraction
PEFT
Safetensors
Transformers
ColPali
English
document-retrieval
multi-vector-embedding
matryoshka
Instructions to use leo-vnuuet/ColQwen3.5-2B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use leo-vnuuet/ColQwen3.5-2B-Embedding with PEFT:
Task type is invalid.
- Transformers
How to use leo-vnuuet/ColQwen3.5-2B-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leo-vnuuet/ColQwen3.5-2B-Embedding")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("leo-vnuuet/ColQwen3.5-2B-Embedding", dtype="auto") - ColPali
How to use leo-vnuuet/ColQwen3.5-2B-Embedding 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
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