Overview

CLIP ViT-L/14@336 is a high-resolution vision-language model designed for precise image-text alignment. By utilizing a Vision Transformer (ViT) with a 14-pixel patch size and a 336px input resolution, it offers significantly better spatial granularity and feature extraction than standard 224px models. For developers, this makes it an ideal backbone for zero-shot image classification, semantic search, and complex image-text retrieval systems. It excels in scenarios where fine-grained visual detail is critical for accurate embedding generation. Integration is straightforward via standard PyTorch or Hugging Face pipelines, serving as a robust encoder for downstream RAG applications or as a guiding signal for generative AI models.

Highlights

High-resolution 336px input for superior visual detail
Optimized for zero-shot image-text retrieval tasks
Strong embedding alignment for semantic search applications
Seamless integration with PyTorch and Hugging Face
Apache-2.0 license for flexible commercial deployment

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("openai/clip-vit-large-patch14-336")
tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-large-patch14-336")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download openai/clip-vit-large-patch14-336

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download openai/clip-vit-large-patch14-336 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('openai/clip-vit-large-patch14-336')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/openai/clip-vit-large-patch14-336

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/openai/clip-vit-large-patch14-336

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('openai/clip-vit-large-patch14-336')
tokenizer = AutoTokenizer.from_pretrained('openai/clip-vit-large-patch14-336')

Full Documentation

来源: HuggingFace

---
tags:

generated_from_keras_callback

widget:

src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png

candidate_labels: playing music, playing sports
example_title: Cat & Dog
model-index:

name: clip-vit-large-patch14-336

results: []
---