Instructions to use imrahamed/coedit-base-webgpu-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use imrahamed/coedit-base-webgpu-onnx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'imrahamed/coedit-base-webgpu-onnx');
Flint CoEdIT Base for WebGPU (ONNX)
Browser-ready ONNX export of the 250M-parameter CoEdIT Base checkpoint, packaged for local sequence-to-sequence generation with Transformers.js and ONNX Runtime WebGPU.
Correct model lineage
This repository is an inference-format conversion, not a newly trained model
and not an export of grammarly/coedit-large.
| Role | Model or dataset |
|---|---|
| Foundation architecture | google/flan-t5-base |
| Fine-tuned checkpoint converted here | jbochi/coedit-base |
| Fine-tuning dataset | grammarly/coedit |
| This ONNX export | imrahamed/coedit-base-webgpu-onnx |
Grammarly publishes the CoEdIT dataset and the official Large, XL, and XXL
checkpoints. It does not publish an official grammarly/coedit-base
checkpoint. The Base checkpoint converted here is the FLAN-T5 Base fine-tune
published by jbochi.
This repository contains only the two graphs required for cached browser generation:
onnx/encoder_model.onnxonnx/decoder_model_merged.onnx
Tokenizer, model configuration, and generation configuration are included at the repository root. The redundant uncached decoder exports are intentionally omitted.
Usage with Transformers.js
import {
AutoModelForSeq2SeqLM,
AutoTokenizer,
} from "@huggingface/transformers";
const modelId = "imrahamed/coedit-base-webgpu-onnx";
const tokenizer = await AutoTokenizer.from_pretrained(modelId);
const model = await AutoModelForSeq2SeqLM.from_pretrained(modelId, {
device: "webgpu",
dtype: "fp32",
});
const input = await tokenizer(
"Fix grammatical errors in this sentence: This are a test.",
);
const output = await model.generate({
inputs: input.input_ids,
attention_mask: input.attention_mask,
max_new_tokens: 64,
});
console.log(
tokenizer.decode(output.tolist()[0], {
skip_special_tokens: true,
}),
);
Expected output: This is a test.
CoEdIT task prompts
| Feature | Prompt |
|---|---|
| Grammar | Fix grammatical errors in this sentence: {text} |
| Formal | Make the sentence formal: {text} |
| Casual | Change the style to casual: {text} |
| Simplify | Make the sentence simpler: {text} |
| Rewrite | Paraphrase the sentence: {text} |
Export details
- Foundation architecture: FLAN-T5 Base
- Converted checkpoint:
jbochi/coedit-base - Fine-tuning dataset:
grammarly/coedit - Parameters: approximately 250M
- Format: ONNX, FP32
- Opset: 18
- Export task:
text2text-generation-with-past - Optimization: Optimum ONNX Runtime
O2 - Intended execution provider: ONNX Runtime WebGPU
- CPU/WASM fallback should be provided by the consuming application.
The export was validated against the source model. Small floating-point differences from ONNX graph optimization may occur.
License and attribution
This derivative export follows the converted checkpoint's Apache 2.0 license.
See the jbochi/coedit-base model
card for its reported training
details and metrics. CoEdIT paper and dataset attribution remains applicable.
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