SeqResize-Qwen2.5-VL-3B

Name: SeqResize_qwen2.5-vl_msrvtt
Rating: 2.5 (18 reviews)

This model uses SeqResize (sequence resizing) to compress multi-vector video representations for ColBERT-style late interaction retrieval. Model weights are initialized from Qwen2.5-VL-3B-Instruct and finetuned on MSR-VTT for text-to-video retrieval with bidirectional attention.

SeqResize compresses ~1300 video token vectors into a fixed budget of 32 vectors (97.6% compression) through projection along the sequence dimension.

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Method Overview

SeqResize is a simple compression baseline: a MLP (or linear) layer projects the sequence dimension from a fixed input length to a fixed output length. Variable-length token sequences are trim-or-padded to the input length, then projected to the target number of vectors for ColBERT-style MaxSim retrieval. It is not the main method in our paper; we include it as a baseline.

Results on MSR-VTT

Method	Tokens	R@1	R@10	nDCG@10
OmniEmbed-7B	1	51.5	83.2	67.1
Video-ColBERT	26	51.5	85.5	67.7
Baseline (Ours, uncompressed)	1318	55.7	88.3	71.9
SeqResize (This model)	32	53.3	86.9	69.9
MemTok	32	54.2	86.4	69.9
H-Pool	32	54.1	87.3	70.4
AGC	32*	56.9	87.0	71.5

Model Details


Initial weights	Qwen2.5-VL-3B-Instruct
Architecture	Qwen2.5-VL with bidirectional attention
Hidden dimension	2048
Budget	32 vectors per document
Compression method	SeqResize (learned sequence projection)
Resizer input size	1024 (fixed sequence length before projection)
Resizer output size	32 (Budget)
Resizer hidden size	256 (MLP bottleneck)
Scoring	ColBERT-style MaxSim (late interaction)
Normalization	L2-normalized embeddings
Query prefix	`"Query: "`
Passage prefix	`"Passage: "`
Precision	bfloat16
Training video frames	24

Usage

Important: When loading this model you must set resizer_input_size, resizer_output_size, and resizer_hidden_size to match the trained checkpoint (1024, 32, and 256 for this release). The extra_encoder_state.safetensors, should also be placed in the model directory so the sequence resizer weights are loaded.

python

import torch
from transformers import AutoProcessor
from qwen_vl_utils import process_vision_info

from src.arguments import ModelArguments
from src.encoder.resize_encoder import SequenceResizerEncoder
from src.models.qwen2_5_vl_embed.qwen2_5_vl_embed import Qwen2_5ForEmbedding

MODEL_ID = "hltcoe/SeqResize_qwen2.5-vl_msrvtt"
VIDEO_PATH = "PLACEHOLDER"
RESIZER_INPUT_SIZE = 1024
RESIZER_OUTPUT_SIZE = 32
RESIZER_HIDDEN_SIZE = 256

# --- Setup ---
model_args = ModelArguments(
    model_name_or_path=MODEL_ID,
    pooling="resize",
    normalize=True,
    resizer_input_size=RESIZER_INPUT_SIZE,
    resizer_output_size=RESIZER_OUTPUT_SIZE,
    resizer_hidden_size=RESIZER_HIDDEN_SIZE,
    attn_implementation="flash_attention_2",
)

processor = AutoProcessor.from_pretrained(MODEL_ID)
model = SequenceResizerEncoder.load(
    Qwen2_5ForEmbedding,
    model_args,
    attn_implementation=model_args.attn_implementation,
    dtype=torch.bfloat16,
)
model = model.to("cuda").eval()

# --- Encode a video document ---
passage_messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Passage: "},
            {"type": "video", "video": VIDEO_PATH, "nframes": 24, "max_pixels": 84672, "min_pixels": 75264},
        ],
    }
]
text = processor.apply_chat_template(passage_messages, tokenize=False, add_generation_prompt=False)
image_inputs, video_inputs = process_vision_info(passage_messages)
passage_inputs = processor(
    text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt",
).to("cuda")

with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
    with torch.inference_mode():
        doc_embeddings, doc_mask = model.encode(passage_inputs, is_query=False)
        print(doc_embeddings.shape)
        # doc_embeddings: (1, 32, 2048) — 32 compressed vectors

# --- Encode a text query ---
query_messages = [{"role": "user", "content": [{"type": "text", "text": "Query: a person is cooking"}]}]
query_text = processor.apply_chat_template(query_messages, tokenize=False, add_generation_prompt=False)
query_inputs = processor(text=[query_text], padding=True, return_tensors="pt").to("cuda")

with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
    with torch.inference_mode():
        query_embeddings, query_mask = model.encode(query_inputs, is_query=True)
        print(query_embeddings.shape)

# --- ColBERT MaxSim scoring ---
score = model.compute_similarity(query_embeddings, doc_embeddings, query_mask, doc_mask)
print(f"Similarity score: {score.item():.4f}")

Command line usage

For running inference and evaluation from the command line, see the Quick Start section.

Citation

bibtex

@misc{qin2026multivectorindexcompressionmodality,
      title={Multi-Vector Index Compression in Any Modality}, 
      author={Hanxiang Qin and Alexander Martin and Rohan Jha and Chunsheng Zuo and Reno Kriz and Benjamin Van Durme},
      year={2026},
      eprint={2602.21202},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2602.21202}, 
}