SauerkrautLM-ColQwen3-4b-v0.1

🥇 Best 128-dim Model in Large (3-5B) Category | Excellent Performance with Half the Memory

SauerkrautLM-ColQwen3-4b-v0.1 achieves 90.80 NDCG@5 on ViDoRe v1, making it the #2 overall among 128-dim models and the best in the Large (3-5B) category for ViDoRe v1.

🎯 Why Visual Document Retrieval?

Traditional OCR-based retrieval loses layout, tables, and visual context. Our visual approach:

• ✅ No OCR errors - Direct visual understanding
• ✅ Layout-aware - Understands tables, forms, charts
• ✅ End-to-end - Single model, no pipeline complexity

🏆 Key Achievements

Large Category Comparison (3-5B, 128-dim)

Best ViDoRe v1 in the Large category!

Detailed Benchmark Results

ViDoRe v1 Tasks:

ViDoRe v2 Tasks (Multilingual):

Overall Summary (128-dim Models)

📋 Summary Tables

128-dim Models Comparison

Comparison vs High-dim Models

✨ Key Features

• 🏆 #2 Overall (128-dim): Second highest ViDoRe v1 score among all 128-dim models
• 🥇 #1 in Large Category: Best 3-5B model on ViDoRe v1
• 💾 Memory Efficient: Only ~8GB VRAM (half of 8B model)
• ⚡ Compact Embeddings: 128-dimensional
• 🌍 Multilingual: 6 languages (EN, DE, FR, ES, IT, PT)

Model Details

Training

Hardware & Configuration

Datasets

Installation & Usage

Sentence Transformers

This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the MultiVectorEncoder:

pip install "sentence-transformers[image]>=6.0.0"

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder(
    "VAGOsolutions/SauerkrautLM-ColQwen3-4b-v0.1",
    model_kwargs={"dtype": "bfloat16"},
)

queries = [
    "What is the variable represented on the y-axis of the graph?",
    "Total outlay is maximum in which year?",
]
images = [
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc1.jpg",
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc2.jpg",
]

query_embeddings = model.encode_query(queries)
image_embeddings = model.encode_document(images)
print(query_embeddings[0].shape, image_embeddings[0].shape)
# torch.Size([25, 128]) torch.Size([1251, 128])

# Diagonal should have higher scores
scores = model.similarity(query_embeddings, image_embeddings)
print(scores)
# tensor([[16.3877,  8.1367],
#         [ 5.8350, 15.3848]], device='cuda:0')

SauerkrautLM ColPali

⚠️ Important: Install our package first before loading the model:

pip install git+https://github.com/VAGOsolutions/sauerkrautlm-colpali

import torch
from PIL import Image
from sauerkrautlm_colpali.models import ColQwen3, ColQwen3Processor

model_name = "VAGOsolutions/SauerkrautLM-ColQwen3-4b-v0.1"

model = ColQwen3.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="cuda:0",
).eval()

processor = ColQwen3Processor.from_pretrained(model_name)

images = [Image.open("document.png")]
queries = ["What is the main topic?"]

batch_images = processor.process_images(images).to(model.device)
batch_queries = processor.process_queries(queries).to(model.device)

with torch.no_grad():
    image_embeddings = model(**batch_images)
    query_embeddings = model(**batch_queries)

scores = processor.score(query_embeddings, image_embeddings)

📊 Additional Benchmark Visualizations

MTEB v1+v2 Benchmark (128-dim Models)

ViDoRe v3 Benchmark (128-dim Models)

Our Models vs High-dim Models

Citation

@misc{sauerkrautlm-colpali-2025,
  title={SauerkrautLM-ColPali: Multi-Vector Vision Retrieval Models},
  author={David Golchinfar},
  organization={VAGO Solutions},
  year={2025},
  url={https://github.com/VAGOsolutions/sauerkrautlm-colpali}
}

Contact

• VAGO Solutions: https://vago-solutions.ai
• GitHub: https://github.com/VAGOsolutions