CRISPR — Checkpoints

Checkpoints for CRISPR: Context-Refined Information Spatial Pooling with Region-awareness for Efficient Visual Token Compression in VLMs, accepted at ACM MM 2026.

• Code: https://github.com/ZuyiZhou/CRISPR
• Paper DOI: https://doi.org/10.1145/3767308.3835007

This repo hosts CRISPR checkpoints for the Qwen2.5-VL-3B-Instruct backbone (9x and 16x compression) and the Qwen2.5-VL-7B-Instruct backbone (16x compression). The 7B/9x checkpoint is not currently available (lost prior to this release) and is not planned unless retraining happens in the future.

Files

Path	Compression ratio	Backbone	Notes
3b_9x/checkpoint.pt	9x (3x3 block)	Qwen2.5-VL-3B-Instruct	Stage-2, best checkpoint by val loss
3b_9x/config.json			training config used to produce this checkpoint
3b_16x/checkpoint.pt	16x (4x4 block)	Qwen2.5-VL-3B-Instruct	Stage-2, best checkpoint by val loss
3b_16x/config.json			training config used to produce this checkpoint
7b_16x/checkpoint.pt	16x (4x4 block)	Qwen2.5-VL-7B-Instruct	Stage-2, best checkpoint by val loss
7b_16x/config.json			training config used to produce this checkpoint

Each checkpoint.pt is a plain torch.save dict with keys config, token_mixer (TokenMixer state dict), and local_c3 (LocalC3 state dict, which also contains the Global Token Fusion sub-module). Only the trainable CRISPR modules are included — the frozen Qwen2.5-VL vision encoder and decoder weights are not part of this checkpoint and must be obtained separately from Qwen2.5-VL. Optimizer/ scheduler state is not included (only the model weights needed for inference or further fine-tuning are provided).

Usage

from crispr import create_model_v7

model = create_model_v7(decoder_path="./Qwen/Qwen2.5-VL-3B-Instruct")
model.load_checkpoint("3b_9x/checkpoint.pt")  # see crispr/model_v7.py for the loader
# for the 7B backbone: decoder_path="./Qwen/Qwen2.5-VL-7B-Instruct", checkpoint="7b_16x/checkpoint.pt"

See the main repository (https://github.com/ZuyiZhou/CRISPR) for the model code, training script, and evaluation scripts.

Citation

@inproceedings{zhou2026crispr,
  author    = {Zhou, Zuyi and Xue, Dizhan and Qian, Shengsheng and Xu, Changsheng},
  title     = {CRISPR: Context-Refined Information Spatial Pooling with
               Region-awareness for Efficient Visual Token Compression in VLMs},
  booktitle = {Proceedings of the 34th ACM International Conference on
               Multimedia (MM '26)},
  year      = {2026},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  doi       = {10.1145/3767308.3835007}
}