This model is an ultra-compact Late Interaction retriever that leverages:
Pretraining with over 8.2 billion tokens in a two-phase approach (4.6B multilingual + 3.6B English tokens). Knowledge Distillation from state-of-the-art reranker models during pretraining. Extreme compression with just 15M parameters – optimized for edge deployment and resource-constrained environments.
Two-Phase Pretraining Strategy:
Advanced Knowledge Distillation: Learning from powerful reranker models throughout the pretraining process
Ultra-Efficient Architecture: With extreme parameter compression to just 15M, enabling deployment on edge devices and mobile platforms
With 15 million parameters – that's less than 1/500th the size of some competing models – SauerkrautLM-Multi-ColBERT-15m represents the extreme frontier of efficient pretraining:
This ultra-compact architecture combined with pretraining creates a powerful foundation for downstream applications.
Model: VAGOsolutions/SauerkrautLM-Multi-ColBERT-15m
Type: Pretrained foundation model for Late Interaction retrieval
Architecture: PyLate / ColBERT (Late Interaction)
Languages: Multilingual (optimized for 7 European languages: German, English, Spanish, French, Italian, Dutch, Portuguese)
License: Apache 2.0
Model Size: 15M parameters
Training Data: 8.2B tokens (4.6B multilingual + 3.6B English)
ColBERT(
(0): Transformer(CompressedModernBertModel)
(1): Dense(384 -> 128 dim, no bias)
)
Our 15M parameter model undergoes sophisticated two-phase pretraining:
Unlike typical pretraining, we leverage continuous knowledge distillation:
SauerkrautLM-Multi-ColBERT-15m achieves extreme efficiency through:
This architecture enables Late Interaction Retrieval on devices previously unable to run neural search models.
Despite its microscopic size, SauerkrautLM-Multi-ColBERT-15m delivers impressive multilingual retrieval performance, demonstrating the power of massive pretraining at extreme compression ratios.
Average nDCG@10 across seven European languages, showing strong multilingual capabilities from our two-phase pretraining:
| Language | nDCG@10 | Performance Notes |
|---|---|---|
| en | 51.09 | Enhanced by Phase 2 English pretraining |
| de | 32.91 | Strong german language performance |
| es | 35.34 | Robust spanish language capabilities |
| fr | 33.84 | Consistent cross-lingual transfer |
| it | 34.26 | Balanced multilingual representation |
| nl | 32.60 | Effective on lower-resource languages |
| pt | 33.81 | Maintains quality across language families |
Key Observations:
This pretrained model serves as an ideal foundation for:
The combination of massive pretraining and extreme efficiency enables:
SauerkrautLM-Multi-ColBERT-15m demonstrates that strong pretraining can be achieved at extreme compression ratios. By training on 8.2 billion tokens across two phases, we've created a model that:
This model proves that thoughtful pretraining strategies can create powerful, efficient foundation models for the age of edge computing.
This is a PyLate model trained. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
First install the PyLate library:
pip install -U pylate
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="VAGOsolutions/SauerkrautLM-Multi-ColBERT-15m",
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="VAGOsolutions/SauerkrautLM-Multi-ColBERT-15m",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
@misc{SauerkrautLM-Multi-ColBERT-15m,
title={SauerkrautLM-Multi-ColBERT-15m},
author={David Golchinfar},
url={https://huggingface.co/VAGOsolutions/SauerkrautLM-Multi-ColBERT-15m},
year={2025}
}
@inproceedings{reimers-2019-sentence-bert,
title = {Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author = {Reimers, Nils and Gurevych, Iryna},
booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
month = {11},
year = {2019},
publisher = {Association for Computational Linguistics},
url = {https://arxiv.org/abs/1908.10084}
}
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
We thank the PyLate team for providing the training framework that made this work possible.