VultronRetrieverPrime is the flagship of the VultronRetriever family, a late-interaction (ColBERT-style) retriever that scores document pages directly from their rendered image: layout, tables, charts and text, across six languages. It ranks first on the full ViDoRe V3 board with 320-dimensional multi-vectors, an 8–12× smaller index than the 2560–4096-dim models below it.

The family has three tiers on one 320-dim recipe: the 8B Prime (this model) for maximum accuracy, the 4.5B Core for the accuracy/footprint mid-point, and the 0.8B Flash for latency- and footprint-sensitive serving. Trained and evaluated on Vultr Cloud.

Highlights

#1 on ViDoRe V3: 64.26 mean nDCG@10 over all 10 tasks (8 public + 2 private), at up to 16× smaller index than the 2560–4096-dim field.
Family 1–2 finish: ranks 1 and 2 on V3 are both VultronRetriever; Core holds #2 at 63.57.
#1 on six of the ten V3 tasks (Energy, FinanceEn, FinanceFr, Industrial, Pharmaceuticals, Physics).
Official MTEB: V1 92.08, V2 68.18, V3 64.26.
8.4B parameters, 320-dim, ~17 GB bf16, runs on one GPU.
Six languages (en, fr, de, es, it, pt).

ViDoRe leaderboard (ranked by V3)

Ranked by ViDoRe V3 (mean nDCG@10), the headline benchmark; V1 and V2 are shown alongside. Our three tiers are in bold; models with a partial or missing V3 sit at the bottom.

Model	Params	Dim	V1	V2	V3
VultronRetrieverPrime-Qwen3.5-8B (this model)	8.4B	320	92.08	68.18	64.26
VultronRetrieverCore-Qwen3.5-4.5B (ours)	4.5B	320	92.21	66.12	63.57
nvidia/nemotron-colembed-vl-8b-v2	8.7B	4096	92.65	65.16	63.42
webAI-Official/webAI-ColVec1-9b	9.4B	2560	91.30	65.82	63.00
webAI-Official/webAI-ColVec1-4b	4.5B	640	90.49	63.60	62.22
TomoroAI/tomoro-colqwen3-embed-8b	8.0B	320	90.76	65.40	61.59
nvidia/nemotron-colembed-vl-4b-v2	4.8B	2560	91.62	64.49	61.54
athrael-soju/colqwen3.5-4.5B-v3	4.6B	128	91.54	64.25	61.46
OpenSearch-AI/Ops-Colqwen3-4B	4.8B	2560	91.36	68.66	61.17
TomoroAI/tomoro-colqwen3-embed-4b	4.0B	320	90.57	64.69	60.20
nvidia/llama-nemotron-colembed-vl-3b-v2	4.4B	3072	91.74	63.38	59.79
jinaai/jina-embeddings-v4	3.9B	2048	90.35	58.23	57.52
nomic-ai/colnomic-embed-multimodal-7b	7.0B	128	89.72	60.25	57.33
nvidia/llama-nemoretriever-colembed-3b-v1	4.4B	3072	91.00	63.32	57.26
VultronRetrieverFlash-Qwen3.5-0.8B (ours)	0.85B	320	88.15	60.36	56.16
Verm1ion/ColTurk-VDR-Qwen3VL-4B-v1.0	4.5B	128	—	—	55.81
nomic-ai/colnomic-embed-multimodal-3b	3.0B	128	89.86	55.68	55.78
nvidia/llama-nemoretriever-colembed-1b-v1	2.4B	2048	90.50	62.96	55.59
vidore/colqwen2.5-v0.2	3.0B	128	89.54	60.06	51.90
VAGOsolutions/SauerkrautLM-ColQwen3-8b-v0.1	8.1B	128	91.08	62.47	58.55†
VAGOsolutions/SauerkrautLM-ColQwen3-4b-v0.1	4.4B	128	90.80	59.89	56.03†
DataScience-UIBK/Argus-Colqwen3.5-9b-v0	8.8B	1024	92.67	69.27	—
DataScience-UIBK/Argus-Colqwen3.5-4b-v0	4.7B	1024	92.30	64.18	—

V1/V2: full mean nDCG@5. V3: mean nDCG@10 over all 10 ViDoRe V3 tasks, the 8 public plus the 2 private tasks (Nuclear, Telecom) scored by the ViDoRe maintainers on held-out corpora. † = evaluated on the 8 public tasks only, not comparable to the 10-task means. Competitor figures: public MTEB ViDoRe leaderboard, 2026-07-04 snapshot; "—" = not reported on that benchmark.

Per-task nDCG@10 (ViDoRe V3)

Prime is #1 on six of the ten tasks; Core takes Nuclear, one of the two private tasks. * = best on that task across the board.

Model	CS	Energy	FinEn	FinFr	Hr	Ind	Nuc	Pharma	Phys	Tel	Mean
VultronRetrieverPrime-Qwen3.5-8B (this model)	79.8	70.3*	69.0*	54.5*	66.8	57.4*	53.6	68.2*	51.7*	71.3	64.26
VultronRetrieverCore-Qwen3.5-4.5B (ours)	79.8	69.2	68.9	52.0	66.1	56.1	54.9*	67.5	50.2	71.1	63.57
nvidia/nemotron-colembed-vl-8b-v2	79.3	69.8	67.3	51.5	66.3	56.0	53.8	67.2	50.8	72.0*	63.42
webAI-Official/webAI-ColVec1-9b	80.9*	69.8	68.3	53.7	70.0*	57.2	47.7	67.3	48.4	66.7	63.00
webAI-Official/webAI-ColVec1-4b	79.8	68.7	68.5	51.1	67.4	55.7	48.2	65.7	50.1	66.9	62.22
TomoroAI/tomoro-colqwen3-embed-8b	75.3	68.4	65.1	49.1	64.0	54.4	52.6	66.4	50.1	70.5	61.59
nvidia/nemotron-colembed-vl-4b-v2	78.6	67.5	65.0	49.0	62.4	53.9	52.8	66.1	48.9	71.3	61.54
athrael-soju/colqwen3.5-4.5B-v3	78.7	68.0	64.1	48.6	62.1	55.2	53.3	65.6	50.3	68.8	61.46

Official MTEB results (dim 320 / visual tokens 1792)

Benchmark	Metric	Tasks	Score
ViDoRe V1	ndcg@5	10	0.9208
ViDoRe V2	ndcg@5	4	0.6818
ViDoRe V3	ndcg@10	10	0.6426

Per-task JSONs for the 8 public V3 tasks are in eval_results/; the 2 private tasks are scored by the ViDoRe maintainers and reported on the public leaderboard. Measured with the official MTEB late-interaction evaluator.

Why 320 dimensions

Late-interaction index size, memory footprint, and MaxSim scoring cost all scale with the embedding dimension. At 320-dim, Prime carries an index 8× smaller than a 2560-dim retriever and roughly 13× smaller than a 4096-dim one, at the same or better ViDoRe V3 accuracy: proportionally lower storage, RAM, and query-time compute at serving scale.

Intended use

Visual document retrieval / multimodal RAG over PDFs, scans, slides and reports, including pages with layout, tables, charts and figures.
Multilingual document collections (en, fr, de, es, it, pt).
Maximum-accuracy retrieval at scale: 320-dim multi-vectors against 2560-4096-dim peers.

Out of scope: text-only semantic search, where a single-vector dense embedder is cheaper; generative QA (this is a retriever; pair it with a reader/LLM).

Method

Per-token MaxSim scoring captures fine-grained matches against tables, figures, and layout that a single-vector embedder averages away.

Base: Qwen/Qwen3.5-9B (hybrid GatedDeltaNet + full-attention backbone).
Late-interaction retriever (ColQwen3_5): 320-dim multi-vector embeddings, MaxSim scoring, image + text inputs.
Size: 8.4B parameters, the generative head dropped for retrieval (HF reports 8B).
Native 320-dim head: trained and operated at 320 dimensions directly.
Hard-negative mining: positive-aware, NV-Retriever-style negative selection on the contrastively-trained constituents.
Model merging: several independently-seeded checkpoints merged per-block into one full-weight checkpoint.
Trained at up to 1280 visual tokens, evaluated and deployed at 1792.

Training data

An enhanced, multilingual mixture of public and synthetic visual-document retrieval sources, spanning en, es, de, fr, it and pt, decontaminated against all three ViDoRe suites (V1/V2/V3): 0% measured overlap with the evaluation benchmarks. The training recipe and the assembled training dataset are not distributed in this repository.

Inputs and outputs

Input: document-page images (RGB) and/or text queries; pages encode at up to 1792 visual tokens.
Output: multi-vector embeddings, one 320-dim vector per token (not a single pooled vector).
Scoring: late-interaction MaxSim between query-token and page-token vectors, via score_multi_vector.

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"

python

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("vultr/VultronRetrieverPrime-Qwen3.5-8B")

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([23, 320]) torch.Size([755, 320])

# Diagonal should have higher scores
scores = model.similarity(query_embeddings, image_embeddings)
print(scores)
# tensor([[15.5859, 10.2539],
#         [ 4.9639, 12.7383]], device='cuda:0')

ColPali Engine

The Qwen3.5 hybrid (GatedDeltaNet + full-attention) backbone has hard runtime kernel dependencies a vanilla ColQwen / PaliGemma card does not:

bash

pip install "git+https://github.com/illuin-tech/colpali@2e0b927051af727238783af039dcc2c50a4d8c27"
pip install causal-conv1d flash-linear-attention

causal-conv1d + flash-linear-attention are required (the hybrid layers import them at runtime).
Use SDPA, matching the evaluation runtime of record.

python

import torch
from PIL import Image
from colpali_engine.models import ColQwen3_5, ColQwen3_5Processor

model = ColQwen3_5.from_pretrained(
    "vultr/VultronRetrieverPrime-Qwen3.5-8B",
    torch_dtype=torch.bfloat16,
    attn_implementation="sdpa",   # required (see above)
    device_map="cuda:0",
).eval()
processor = ColQwen3_5Processor.from_pretrained(
    "vultr/VultronRetrieverPrime-Qwen3.5-8B",
    max_num_visual_tokens=1792,
)

# Document pages (rendered to images) and text queries
images = [Image.open("page_0.png"), Image.open("page_1.png")]
queries = ["What was Q3 revenue?", "Summarize the safety findings."]

with torch.no_grad():
    doc_emb = model(**processor.process_images(images).to(model.device))
    qry_emb = model(**processor.process_queries(queries).to(model.device))

# Late-interaction MaxSim scoring (feed fp32 to match the eval discipline)
scores = processor.score_multi_vector(qry_emb.float(), doc_emb.float())
# scores[i, j] = relevance of query i to page j
print(scores.shape)  # torch.Size([2, 2])

config.json carries dim=320, so custom_text_proj is sized correctly at load, with no manual config edits needed. It also carries retrieval_attention_contract="causal"; a conflicting runtime override is an error.

vLLM

vLLM serves this model natively through its pooling runner (the ColQwen3_5 architecture), returning the per-token multi-vectors for late-interaction scoring. It requires a vLLM build that includes the projection-bias fix and explicit retrieval-attention metadata support. The handler reads retrieval_attention_contract="causal" from this checkpoint and constructs all eight full-attention layers as AttentionType.DECODER; it must reject missing, conflicting, or unsupported contract metadata. The server uses the stock chat/image processor, so the ColQwen3.5 prompt contract is applied client-side: wrap each page image in the instruction template, append the query-augmentation tokens to each query, and set the visual-token budget through mm-processor-kwargs. Prefix caching and chunked prefill remain off for the GatedDeltaNet hybrid.

pip install vllm

python

import torch
from PIL import Image
from vllm import LLM
MODEL = "vultr/VultronRetrieverPrime-Qwen3.5-8B"
MAX_PIXELS = 1792 * 32 * 32   # max_num_visual_tokens * (patch_size 16 * merge_size 2)^2
llm = LLM(
    model=MODEL,
    runner="pooling",
    dtype="bfloat16",
    enable_prefix_caching=False,
    enable_chunked_prefill=False,
    mm_processor_kwargs={"min_pixels": 65536, "max_pixels": MAX_PIXELS},
)
# ColQwen3.5 processor contract, applied client-side:
IMAGE_PROMPT = ("<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
                "Describe the image.<|im_end|><|endoftext|>")
def query_prompt(q): return q + "<|endoftext|>" * 10   # query augmentation
images  = [Image.open("page_0.png"), Image.open("page_1.png")]
queries = ["What was Q3 revenue?", "Summarize the safety findings."]
doc_out = llm.encode([{"prompt": IMAGE_PROMPT, "multi_modal_data": {"image": im}}
                      for im in images], pooling_task="token_embed")
qry_out = llm.encode([query_prompt(q) for q in queries], pooling_task="token_embed")
def mv(o):  # one [num_tokens, 320] multi-vector per item, L2-normalized per token
    t = torch.as_tensor(o.outputs.data, dtype=torch.float32)
    return torch.nn.functional.normalize(t, p=2, dim=-1)
docs, qrys = [mv(o) for o in doc_out], [mv(o) for o in qry_out]
# late-interaction MaxSim: per query token take the best doc token, then sum
scores = [[(q @ d.T).max(dim=-1).values.sum().item() for d in docs] for q in qrys]
print(scores)  # scores[i][j] = relevance of query i to page j

To serve over HTTP instead:

bash

vllm serve vultr/VultronRetrieverPrime-Qwen3.5-8B \
  --runner pooling \
  --no-enable-prefix-caching --no-enable-chunked-prefill \
  --mm-processor-kwargs '{"min_pixels": 65536, "max_pixels": 1835008}'

Apply the same image template and query augmentation in your client requests. See the upstream example examples/pooling/score/colqwen3_5_rerank_online.py for the full online rerank flow.

Limitations

Tuned for six languages (en, fr, de, es, it, pt); other languages are out of distribution.
Late-interaction multi-vector indexes are larger than single-vector dense indexes: the trade for per-token layout/table/figure sensitivity (small for its class at 320-dim).

License

Apache 2.0, covering the contents of this repository: model weights, config, and evaluation results. Built on Qwen/Qwen3.5-9B (Apache 2.0); the upstream license and attribution are retained. The training recipe and the assembled training dataset are not distributed in this repository.

Citation

bibtex

@misc{vultronretrieverprime2026,
  title  = {VultronRetrieverPrime-Qwen3.5-8B: Late-Interaction Visual Document Retrieval at 320 Dimensions},
  author = {Georgiou, Athos (athrael-soju)},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/vultr/VultronRetrieverPrime-Qwen3.5-8B}}
}

Trained and evaluated on Vultr Cloud.