This model is an isotropically corrected version of GTE-ModernColBERT-v1. It's built for production use cases where retrieval speed and quality matter. Compared to the original model, this version delivers up to 3x faster inference in bf16 with almost no loss in accuracy and enables scalable multi-vector retrieval through Sparse Multi-Vector Encoding (SMVE) inside TopK.

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>=6.0.0"

python

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("topk-io/Iso-ModernColBERT")

query = "Which planet is known as the Red Planet?"
documents = [
    "Venus is often called Earth's twin because of its similar size and proximity.",
    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    "Jupiter, the largest planet in our solar system, has a prominent red spot.",
    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings[0].shape)
# (12, 128) (18, 128)

# MaxSim late-interaction scoring (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[ 9.4844, 10.4180,  9.8516, 10.1953]])

PyLate

Install PyLate for embeddings and TopK SDK for retrieval.

pip install -U pylate topk-sdk

Embed documents

First, load the model into PyLate ColBERT class and encode your documents.

python

import torch
import numpy as np
from pylate import models


model = models.ColBERT(
  model_name_or_path="topk-io/Iso-ModernColBERT",
  model_kwargs={'torch_dtype': torch.bfloat16},
)

documents = [
  "document 1 text",
  "document 2 text",
  "document 3 text",
]

doc_embeddings = model.encode(
    documents,
    batch_size=32,
    # Ensure that it is set to False to indicate that these are documents, not queries
    is_query=False,
    show_progress_bar=True,
)

Store document embeddings

Index multi-vector document embeddings inside TopK, hybrid retrieval engine built on object storage. To get started, create an API key.

python

from topk_sdk import Client
from topk_sdk.schema import matrix, multi_vector_index

# Initialize TopK client
client = Client(
    api_key = "<TOPK_API_KEY>",
    region = "aws-us-east-1-elastica",
)

# Create a collection with multi-vector index
client.collections().create(
    "iso-moderncolbert",
    schema = {
        "token_embeddings": matrix(dimension=128, value_type="f16")
            .index(multi_vector_index(metric="maxsim"))
    }
)

# Upsert document embeddings
client.collection("iso-moderncolbert").upsert([
  {
    "_id": str(i),
    "token_embeddings": emb.astype(np.float16),
    "text": text
  }
  for (i, (text, emb)) in enumerate(zip(documents, doc_embeddings))
])

Retrieve documents for queries

Your documents are now durably persisted in the index and queryable.

python

from topk_sdk.query import fn, select, field

# Encode query string
query_embedding = model.encode(
    "query for document 3",
    # Ensure that it is set to True for queries
    is_query=True,
    show_progress_bar=False,
)

# Retrieve top-k documents using the query embedding
results = client.collection("iso-moderncolbert").query(
    select(
        "_id", "text",
        # Compute maxsim between query and indexed documents
        maxsim_score = fn.multi_vector_distance(
            "token_embeddings",
            query_embedding.astype(np.float16)
        )
    )
    # Get the top 10 matching documents
    .topk(field("maxsim_score"), 10)
)

for r in results:
    print(f"id: {r['_id']}, score: {r['maxsim_score']}, text: {r['text']}")

TopK's query language is flexible and allows you to tune retrieval parameters, combine multi-vector with metadata filters, keyword search, and more. Check out our docs to learn more.

Evaluation results

We conducted evaluation of our model using an internal evaluation harness on two standard benchmarks - BEIR and NanoBEIR. For baselines, we selected GTE-ModernColBERT-v1 and evaluated its perfomance in fp32 and bf16 precision (denoted by GTE fp32 and GTE bf16, respectively). The last two columns of each table — Iso bf16 and Δ vs GTE — describe Iso-ModernColBERT (ours) in bf16 precision. In all configurations we used the same SMVE implementation with width 65536 and k=32.

BEIR

NDCG@10 — ranking quality is robust to bf16

End-to-end ranking quality reported as NDCG@10, using exact MaxSim scoring (no approximation). GTE-ModernColBERT-v1 loses ~7 NDCG points on average going from fp32 → bf16 — about a 13% relative drop — with the worst-hit datasets (trec-covid, climate-fever, hotpotqa) dropping 12–16 points. Iso-ModernColBERT keeps fp32-level ranking quality in bf16, recovering most of that gap on average and on every dataset.

dataset	GTE fp32 N@10	GTE bf16 N@10	Iso bf16 N@10	Δ vs GTE bf16
arguana	35.81%	30.35%	34.63%	+14.10%
climate-fever	32.44%	19.49%	31.62%	+62.24%
cqadupstack	40.54%	38.25%	40.64%	+6.25%
dbpedia	53.96%	48.43%	52.84%	+9.11%
fever	88.80%	80.67%	87.08%	+7.95%
fiqa	45.56%	37.15%	43.48%	+17.04%
hotpotqa	78.36%	66.74%	75.85%	+13.65%
msmarco	46.12%	41.82%	45.30%	+8.32%
nfcorpus	37.81%	35.98%	37.31%	+3.70%
nq	62.24%	52.60%	60.45%	+14.92%
quora	86.63%	79.58%	85.05%	+6.87%
scidocs	19.49%	17.82%	18.81%	+5.56%
scifact	75.98%	71.55%	75.26%	+5.18%
touche2020	31.30%	22.93%	29.45%	+28.43%
trec-covid	89.30%	73.47%	83.76%	+14.01%
avg	54.96%	47.79%	53.44%	+11.82%

Recall@100 — SMVE as a first stage with ~10× overfetch

The following results show model performance when used with Sparse Multi-Vector Encoder (SMVE) as a first stage retriever.

For a SMVE first stage to be usable, it needs to surface the candidates that the exact fp32 MaxSim model would have ranked at the top. SMVE on GTE-ModernColBERT-v1 is broken — its compacted latent geometry means random anchors don't separate vectors well. Iso-ModernColBERT's SMVE recovers (and often exceeds) the fp32 MaxSim top-10 within 10× overfetch.

dataset	GTE fp32 MaxSim R@10	GTE fp32 SMVE R@100	Iso bf16 SMVE R@100	Δ vs GTE fp32 SMVE
arguana	72.81%	27.69%	84.51%	+205.20%
climate-fever	39.27%	0.41%	48.84%	+11,812% ⚠
cqadupstack	50.48%	11.78%	37.29%	+216.55%
dbpedia	30.45%	8.54%	36.89%	+331.97%
fever	94.20%	10.05%	94.31%	+838.41%
fiqa	52.15%	6.45%	49.12%	+661.55%
hotpotqa	80.73%	12.29%	66.59%	+441.82%
msmarco	68.64%	27.77%	75.83%	+173.07%
nfcorpus	18.03%	16.63%	25.60%	+53.94%
nq	82.03%	14.60%	78.85%	+440.07%
quora	94.92%	43.73%	82.86%	+89.48%
scidocs	20.36%	12.29%	29.32%	+138.57%
scifact	87.39%	60.93%	90.00%	+47.71%
touche2020	19.69%	4.47%	40.17%	+798.66%
trec-covid	2.27%	0.89%	7.73%	+768.54%
avg	54.23%	17.23%	56.53%	+228.09%

⚠ The +11,812% on climate-fever is an artifact of a near-zero baseline (0.41%): GTE's SMVE is so broken on that dataset that the ratio explodes. Read it as "GTE SMVE doesn't work here at all", not as a meaningful magnitude.

Recall@1000 — SMVE as a first stage with ~10× overfetch (deeper pool)

Same picture at the next pool depth: Iso-ModernColBERT SMVE R@1000 essentially matches or exceeds fp32 MaxSim R@100 across the board, while GTE's SMVE collapses.

dataset	GTE fp32 MaxSim R@100	GTE fp32 SMVE R@1000	Iso bf16 SMVE R@1000	Δ vs GTE fp32 SMVE
arguana	95.72%	68.31%	97.00%	+42.00%
climate-fever	66.45%	0.93%	68.87%	+7,305% ⚠
cqadupstack	71.44%	26.78%	55.78%	+108.29%
dbpedia	62.50%	18.35%	57.72%	+214.55%
fever	97.46%	16.74%	96.91%	+478.91%
fiqa	75.64%	21.09%	76.70%	+263.68%
hotpotqa	90.31%	22.72%	78.83%	+247.05%
msmarco	93.14%	46.57%	90.97%	+95.34%
nfcorpus	32.22%	49.11%	57.16%	+16.39%
nq	96.59%	29.88%	91.42%	+205.96%
quora	99.45%	69.38%	94.86%	+36.72%
scidocs	44.07%	32.62%	53.43%	+63.80%
scifact	96.00%	89.82%	99.33%	+10.59%
touche2020	52.60%	13.91%	69.63%	+400.58%
trec-covid	16.02%	3.85%	29.57%	+668.05%
avg	72.64%	34.00%	74.55%	+119.26%

⚠ Again, climate-fever's +7,305% is driven by a near-zero baseline (0.93%) — GTE SMVE simply doesn't work on this dataset.

NanoBEIR

NDCG@10 — ranking quality is robust to bf16

End-to-end ranking quality reported as NDCG@10, using exact MaxSim scoring (no approximation). GTE-ModernColBERT-v1 drops ~6 NDCG points on average going from fp32 → bf16 — about a 9% relative drop — with some datasets (ArguAna, ClimateFEVER, FiQA, Touche2020) losing 8–13 points. Iso-ModernColBERT keeps fp32-level ranking quality in bf16 — average is within 0.6 points of fp32, and most per-dataset gaps close to a few percent.

dataset	GTE fp32 N@10	GTE bf16 N@10	Iso bf16 N@10	Δ vs GTE bf16
ArguAna	51.98%	43.50%	54.31%	+24.85%
ClimateFEVER	40.46%	27.78%	38.17%	+37.40%
DBPedia	72.82%	70.39%	71.56%	+1.66%
FEVER	94.52%	89.82%	93.23%	+3.80%
FiQA2018	56.64%	44.13%	55.79%	+26.42%
HotpotQA	89.95%	85.64%	90.47%	+5.64%
MSMARCO	70.89%	68.77%	72.56%	+5.51%
NFCorpus	39.58%	39.20%	38.67%	-1.35%
NQ	77.19%	69.01%	73.64%	+6.71%
QuoraRetrieval	97.08%	90.60%	96.53%	+6.54%
SCIDOCS	39.85%	38.02%	38.14%	+0.32%
SciFact	82.98%	80.45%	83.32%	+3.57%
Touche2020	59.34%	48.67%	58.77%	+20.75%
avg	67.18%	61.23%	66.55%	+8.69%

Recall@100 — SMVE as a first stage with ~10× overfetch

The following results show model performance when used with Sparse Multi-Vector Encoder (SMVE) as a first stage retriever.

dataset	GTE fp32 MaxSim R@10	GTE fp32 SMVE R@100	Iso bf16 SMVE R@100	Δ vs GTE fp32 SMVE
ArguAna	80.00%	32.00%	90.00%	+181.25%
ClimateFEVER	47.07%	20.67%	66.97%	+224.00%
DBPedia	41.21%	49.00%	72.85%	+48.67%
FEVER	98.00%	61.33%	98.00%	+59.79%
FiQA2018	64.12%	23.25%	78.93%	+239.48%
HotpotQA	92.00%	46.00%	90.00%	+95.65%
MSMARCO	92.00%	84.00%	98.00%	+16.67%
NFCorpus	15.66%	16.33%	24.58%	+50.52%
NQ	88.00%	70.00%	95.00%	+35.71%
QuoraRetrieval	98.93%	87.93%	96.60%	+9.86%
SCIDOCS	39.67%	37.87%	61.17%	+61.53%
SciFact	93.00%	57.50%	92.00%	+60.00%
Touche2020	33.52%	33.55%	69.86%	+108.23%
avg	67.94%	47.65%	79.53%	+66.91%

Recall@1000 — SMVE as a first stage with ~10× overfetch (deeper pool)

Same picture at the next pool depth: Iso-ModernColBERT SMVE R@1000 essentially matches or exceeds fp32 MaxSim R@100 across the board, while GTE's SMVE consistently undershoots.

dataset	GTE fp32 MaxSim R@100	GTE fp32 SMVE R@1000	Iso bf16 SMVE R@1000	Δ vs GTE fp32 SMVE
ArguAna	96.00%	80.00%	100.00%	+25.00%
ClimateFEVER	81.17%	68.80%	89.03%	+29.40%
DBPedia	85.58%	84.85%	96.20%	+13.38%
FEVER	100.00%	94.33%	99.00%	+4.95%
FiQA2018	86.82%	72.61%	91.35%	+25.81%
HotpotQA	97.00%	84.00%	98.00%	+16.67%
MSMARCO	100.00%	98.00%	100.00%	+2.04%
NFCorpus	30.55%	52.82%	59.33%	+12.32%
NQ	100.00%	91.00%	100.00%	+9.89%
QuoraRetrieval	100.00%	96.00%	100.00%	+4.17%
SCIDOCS	70.67%	78.93%	90.80%	+15.04%
SciFact	96.00%	93.00%	100.00%	+7.53%
Touche2020	77.23%	80.46%	93.09%	+15.70%
avg	86.23%	82.68%	93.60%	+13.21%