Image-Text-to-Text
Safetensors
ColPali
sentence-transformers
sauerkrautlm-colpali
mistral3
document-retrieval
vision-language-model
multi-vector
late-interaction
visual-retrieval
ministral
pixtral
mistral
mteb
vidore
conversational
Instructions to use VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1 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
- sentence-transformers
How to use VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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**🔬 Experimental Architecture** | **Mistral-Based Visual Retrieval**
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SauerkrautLM-ColMinistral3-3b-v0.1 is an **experimental** model based on Ministral-3B-
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> ⚠️ **Note**: This is an experimental release. For production use, we recommend ColQwen3 or ColLFM2 models.
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**🔬 Experimental Architecture** | **Mistral-Based Visual Retrieval**
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SauerkrautLM-ColMinistral3-3b-v0.1 is an **experimental** model based on mistralai/Ministral-3-3B-Reasoning-2512 with the Pixtral vision encoder, exploring the Mistral architecture for document retrieval.
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> ⚠️ **Note**: This is an experimental release. For production use, we recommend ColQwen3 or ColLFM2 models.
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