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onnx-community/
granite-timeseries-patchtst

Name: granite-timeseries-patchtst
Availability: InStock

transformers.js

onnx

patchtst

time-series-forecasting

ibm-granite/granite-timeseries-patchtst

quantized

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https://huggingface.co/ibm-granite/granite-timeseries-patchtst 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: Time series forecasting w/ onnx-community/granite-timeseries-patchtst

import { PatchTSTForPrediction, Tensor } from "@huggingface/transformers";

const model_id = "onnx-community/granite-timeseries-patchtst";
const model = await PatchTSTForPrediction.from_pretrained(model_id, { dtype: "fp32" });

const dims = [64, 512, 7];
const prod = dims.reduce((a, b) => a * b, 1);
const past_values = new Tensor('float32',
    Float32Array.from({ length: prod }, (_, i) => i / prod),
    dims,
);
const { prediction_outputs } = await model({ past_values });
console.log(prediction_outputs);

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).