Instructions to use markdownnn/recall-granite-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use markdownnn/recall-granite-q8 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'markdownnn/recall-granite-q8');
recall-granite-q8
INT8-quantized ONNX build of
ibm-granite/granite-embedding-107m-multilingual,
prepared for fully on-device semantic search in
Recall โ a privacy-first Chrome extension that
re-finds the pages you've read, entirely in your browser (zero network egress).
What this is
- Base model:
ibm-granite/granite-embedding-107m-multilingual(Apache-2.0) - Quantization: dynamic INT8 via ONNX Runtime /
optimumโ 384-dim embeddings - Runtime: runs locally in the browser with
transformers.js+onnxruntime-web(WebGPU, with a WASM fallback). No server, no data leaves the device. - Files:
onnx/model_quantized.onnx,tokenizer.json,config.json,tokenizer_config.json
The Recall extension downloads these files once (or builds them via the included
scripts/quantize-granite.py recipe) and bundles them; all capture, embedding, and search
then happen on-device.
Quantization recipe
Reproduced from IBM's official fp32 release โ ONNX export โ optimum AVX2 dynamic INT8.
See scripts/quantize-granite.py in the Recall repo.
License
Apache-2.0, inherited from the base model. This is a quantized derivative; all credit for the underlying model goes to IBM Granite. See the base model card for full terms.
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