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CLIMP Mamba2

Contrastive Language-Image Mamba Pretraining (CLIMP) using Mamba2-1.3B as text encoder.

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Model source

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Text model
File size
5.34 GB
Version
vmodel
Updated
Sep 17, 2026
Downloads
6
Source
Hugging Face

Source description

CLIMP-Mamba2

Contrastive Language-Image Mamba Pretraining (CLIMP) using Mamba2-1.3B as text encoder.

Model Description

ComponentDetails
Vision EncoderVMamba-Base (128-256-512-1024 dims, depths [2,2,15,2])
Text EncoderMamba2-1.3B (AntonV/mamba2-1.3b-hf)
Projection Dim768
Training DataCC12M
Image Resolution224x224
LossSymmetric InfoNCE (learned temperature)

Usage

from models import load_climp
from data.utils import transform_image

model = load_climp("mamba2")
transform = transform_image(224)

See the demo repository for evaluation code.

Paper

CLIMP: Contrastive Language-Image Mamba Pretraining

@article{climp2026,
  title={CLIMP: Contrastive Language-Image Mamba Pretraining},
  author={Shabtay, Nimrod and Zimerman, Itamar and Schwartz, Eli and Giryes, Raja},
  journal={arXiv preprint arXiv:2601.06891},
  year={2026}
}

Sources

1 source
Hugging FacePrimaryVersion fc6aba73e4db040a73fa2d1c406a4e1ac244e484
huggingface.co/NimrodShabtay1986/CLIMP-Mamba2/tree/fc6aba73e4db040a73fa2d1c406a4e1ac244e484

Verified Sep 3

Model artifacts

1 artifact

model.safetensors

safetensors · 5.34 GB · SHA-256 77294e6e39ef…abdd · Hugging Face

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Source excerpts

2 excerpts

--- license: apache-2.0 tags: - clip - mamba - mamba2 - vision-language - contrastive-learning - vmamba datasets: - conceptual_12m pipeline_tag: zero-shot-image-classification --- # CLIMP-Mamba2 **C**ontrastive **L**anguage-**I**mage **M**amba **P**retraining (CLIMP) using Mamba2-1.3B as text encoder. ## Model Description | Component | Details | |-----------|---------| | Vision Encoder | VMamba-Base (128-256-512-1024 dims, depths [2,2,15,2]) | | Text Encoder | Mamba2-1.3B (AntonV/mamba2-1.3b-hf) | | Projection Dim | 768 | | Training Data | CC12M | | Image Resolution | 224x224 | | Loss | Symmetric InfoNCE (learned temperature) | ## Usage ```python from models import load_climp from data.utils import transform_image model = load_climp("mamba2") transform = transform_image(224) ``` See the [demo repository](https://github.com/NimrodShabtay/CLIMP) for evaluation code. ## Paper [CLIMP: Contrastive Language-Image Mamba Pretraining](https://arxiv.org/abs/2601.06891) ```bibtex @article{climp2026, title={CLIMP: Contrastive Language-Image Mamba Pretraining}, author={Shabtay, Nimrod and Zimerman, Itamar and Schwartz, Eli and Giryes, Raja}, journal={arXiv preprint arXiv:2601.06891}, year={2026} } ```

Source context: 6 downloads · 0 likes · Pipeline zero-shot-image-classification · Repo NimrodShabtay1986/CLIMP-Mamba2