# CLIP-ViT-B-32-laion2B-s34B-b79K

Source: https://zilliz.com/ai-models/clip-vit-b-32-laion2b-s34b-b79k
Published: 2024-11-01T19:44:05.934Z

## Model attributes

| Attribute | Value |
| --- | --- |
| Provider | Laion |
| Task | Embedding |
| Modality | Multimodal |
| Similarity metric | Cosine |
| License | Mit |
| Dimensions | 512 |
| Max input tokens | 77 |
| Price | Free |

## Overview of CLIP-ViT-B-32-laion2B-s34B-b79K

The **CLIP-ViT-B-32-laion2B-s34B-b79K** is a **multimodal embedding model** developed by [LAION](https://laion.ai/). This model is trained to understand the relationships between text and images using the English subset of the Laion-5B dataset. It can be used for various [computer vision](https://zilliz.com/learn/what-is-computer-vision) and retrieval tasks, including zero-shot [image classification](https://zilliz.com/learn/deep-residual-learning-for-image-recognition), where it can classify images based on text descriptions without additional training, [image-text retrieval](https://zilliz.com/blog/combine-image-and-text-how-multimodal-retrieval-transforms-search) for finding similar images or texts, and image segmentation to identify and segment objects within images based on semantic meanings.


## How to create embeddings using CLIP-ViT-B-32-laion2B-s34B-b79K

You can use CLIP-ViT-B-32-laion2B-s34B-b79K for embedding through OpenCLIP.

Once the vector embeddings are created, they can be stored in a vector database like [Zilliz Cloud](https://zilliz.com/cloud) (a fully managed vector database powered by [Milvus](https://milvus.io/) and used for image search or multimodal search.

Here are four key steps:

- [Sign up](https://cloud.zilliz.com/signup) for a Zilliz Cloud account for free.
- [Set up a serverless cluster](https://docs.zilliz.com/docs/create-cluster#set-up-a-free-cluster) and obtain the [Public Endpoint and API Key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details).
- Create a vector collection and insert your vector embeddings.
- Run a semantic search on the stored embeddings.


### Create embeddings via OpenCLIP and insert them in Zilliz Cloud for multimodal search

A step-by-step guide: coming soon. 


## Further reading

- [How Multimodal Retrieval Transforms Image Search](https://zilliz.com/blog/combine-image-and-text-how-multimodal-retrieval-transforms-search)
- [Choosing the Right Embedding Model for Your Data](https://zilliz.com/blog/choosing-the-right-embedding-model-for-your-data)
- [Evaluating Your Embedding Model](https://zilliz.com/learn/evaluating-your-embedding-model)
- [Training Your Own Text Embedding Model](https://zilliz.com/learn/training-your-own-text-embedding-model)
- [A Beginner's Guide to Website Chunking and Embedding for Your RAG Applications](https://zilliz.com/learn/beginner-guide-to-website-chunking-and-embedding-for-your-genai-applications)
- [What is RAG?](https://zilliz.com/learn/Retrieval-Augmented-Generation)