SauerkrautLM-ColLFM2-450M-v0.1

🏆 #1 Small Model (<1B) | Best-in-Class Efficiency

SauerkrautLM-ColLFM2-450M-v0.1 is the #1 small model for visual document retrieval, achieving 83.56 NDCG@5 on ViDoRe v1 - beating colSmol-500M (82.49) with 10% fewer parameters!

🎯 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

Small Category Comparison (<1B, 128-dim)

#1 in ALL benchmarks for small models!

Detailed Benchmark Results

📊 ViDoRe v1 (NDCG@5) - Click to expand

Task	Score
ArxivQA	76.11
DocVQA	59.11
InfoVQA	88.36
ShiftProject	73.14
SyntheticDocQA-AI	98.76
SyntheticDocQA-Energy	94.39
SyntheticDocQA-Gov	94.61
SyntheticDocQA-Health	97.32
TabFQuAD	80.91
TATDQA	72.88
Average	83.56

📊 MTEB v1+v2 (NDCG@5) - Click to expand

ViDoRe v1 Tasks:

Task	Score
ArxivQA	76.11
DocVQA	59.11
InfoVQA	88.36
ShiftProject	73.14
SyntheticDocQA-AI	98.76
SyntheticDocQA-Energy	94.39
SyntheticDocQA-Gov	94.61
SyntheticDocQA-Health	97.32
TabFQuAD	80.91
TATDQA	72.88

ViDoRe v2 Tasks (Multilingual):

Task	Score
ViDoRe-v2-2BioMed	51.00
ViDoRe-v2-2Econ	48.35
ViDoRe-v2-2ESG-HL	54.87
ViDoRe-v2-2ESG	50.80
Combined Average	74.33

📊 ViDoRe v3 (NDCG@10) - Click to expand

Task	Score
ViDoRe-v3-CS	58.08
ViDoRe-v3-Energy	47.92
ViDoRe-v3-FinanceEn	47.72
ViDoRe-v3-FinanceFr	33.00
ViDoRe-v3-HR	43.37
ViDoRe-v3-Industry	30.21
ViDoRe-v3-Pharma	51.42
ViDoRe-v3-Physics	34.83
Average	43.32

Efficiency Comparison

📋 Summary Tables

128-dim Models Comparison

Comparison vs High-dim Models

✨ Key Features

• 🏆 Best Small Model: #1 in ALL benchmarks for <1B models
• ⚡ Ultra Efficient: Only 450M parameters, ~0.9GB VRAM
• 🎓 Curriculum Learning: Trained with progressive difficulty
• 🔀 Hierarchical Merge: Advanced model merging for optimal performance
• 📐 Native 512x512: Optimized for document resolution
• 🌍 Multilingual: 6 languages (EN, DE, FR, ES, IT, PT)

Model Details

🎓 Advanced Training Methodology

1. Curriculum Learning

Unlike standard training, ColLFM2 was trained with curriculum learning:

Stage 1: Easy examples (high-quality, clear documents)
    ↓
Stage 2: Medium examples (mixed quality)
    ↓
Stage 3: Hard examples (complex layouts, noisy scans)
    ↓
Stage 4: Full mixture with hard negatives

2. Hierarchical Model Merging

Base LFM2-VL-450M
        ↓
    ┌───┴───┐
    ↓       ↓
mMARCO   Retrieval
Specialist  Model
    ↓       ↓
    └───┬───┘
        ↓
  Hierarchical Merge
        ↓
   Final Model

• mMARCO Specialist: Sub-model trained on mMARCO for retrieval fundamentals
• Retrieval Model: Trained on document retrieval datasets
• Hierarchical Merge: Combined using learned merge weights

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-ColLFM2-450M-v0.1")

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([14, 128]) torch.Size([1792, 128])

# Diagonal should have higher scores
scores = model.similarity(query_embeddings, image_embeddings)
print(scores)
# tensor([[13.5820, 13.4766],
#         [ 9.2461,  9.5703]], 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 ColLFM2, ColLFM2Processor

model_name = "VAGOsolutions/SauerkrautLM-ColLFM2-450M-v0.1"

model = ColLFM2.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="cuda:0",
).eval()

processor = ColLFM2Processor.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)

Use Cases

✅ Perfect for:

• Edge deployment (Raspberry Pi, Jetson)
• Mobile applications
• High-throughput batch processing
• Cost-sensitive deployments
• Real-time retrieval systems

📊 Additional Benchmark Visualizations

MTEB v1+v2 Benchmark (128-dim Models)

ViDoRe v3 Benchmark (128-dim Models)

Our Models vs High-dim Models

License

This model is licensed under the LFM 1.0 License from LiquidAI. Please review the full license before commercial use.

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