Xenova/dit-base
By Xenova · ↓ 7 · ♥ 0 · Updated 2025-07-10
What's in this repo
This page lists the files in Xenova/dit-base, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 7 ONNX models (.onnx files)
- 12 files, including 7 .onnx, 3 .json, 1 .md
- Last updated 2025-07-10
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
https://huggingface.co/microsoft/dit-base with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Perform image feature extraction.
import { pipeline } from '@huggingface/transformers';
const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/dit-base');
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
const features = await image_feature_extractor(url);
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
Model info
Created: 2023-12-24
Updated: 2025-07-10
View on Hugging Face