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MODEL Listed

CLIP ViT B 32 laion2B s34B b79K

CLIP ViT-B/32 (trained on LAION-2B) is a robust vision-language model designed for high-performance image-text alignment. By leveraging a Vision Transformer (ViT) backbone, it maps images and text into a shared embedding space, allowing developers to calculate cosine similarity for efficient retrieval and zero-shot classification. This specific variant is optimized for scale, making it ideal for building semantic search engines, automated image tagging systems, or as a visual encoder for larger multimodal architectures. Compared to smaller CLIP models, it offers a strong balance between inference latency and retrieval accuracy, integrating seamlessly into PyTorch and Hugging Face pipelines for rapid deployment in production environments.

laionimage-text-retrieval
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01 / MODEL CARD

Model card

CLIP ViT-B/32 (trained on LAION-2B) is a robust vision-language model designed for high-performance image-text alignment. By leveraging a Vision Transformer (ViT) backbone, it maps images and text into a shared embedding space, allowing developers to calculate cosine similarity for efficient retrieval and zero-shot classification. This specific variant is optimized for scale, making it ideal for building semantic search engines, automated image tagging systems, or as a visual encoder for larger multimodal architectures. Compared to smaller CLIP models, it offers a strong balance between inference latency and retrieval accuracy, integrating seamlessly into PyTorch and Hugging Face pipelines for rapid deployment in production environments.

Model typeimage-text-retrieval
Providerlaion
Licensemit
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: laion/CLIP-ViT-B-32-laion2B-s34B-b79K
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model laion/CLIP-ViT-B-32-laion2B-s34B-b79K
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model laion/CLIP-ViT-B-32-laion2B-s34B-b79K README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('laion/CLIP-ViT-B-32-laion2B-s34B-b79K')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/laion/CLIP-ViT-B-32-laion2B-s34B-b79K.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/laion/CLIP-ViT-B-32-laion2B-s34B-b79K.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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