Instructions to use Xenova/texify2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/texify2 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-to-text', 'Xenova/texify2');
File size: 3,258 Bytes
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"per_channel": false,
"reduce_range": false,
"per_model_config": {
"encoder_model": {
"op_types": [
"Range",
"Concat",
"Expand",
"Constant",
"Not",
"Sub",
"Transpose",
"Mul",
"ConstantOfShape",
"Erf",
"Add",
"Cast",
"MatMul",
"Slice",
"Pad",
"Conv",
"Pow",
"Unsqueeze",
"Div",
"ReduceMean",
"Mod",
"Where",
"Gather",
"Softmax",
"ScatterND",
"Reshape",
"Equal",
"Sqrt",
"Shape"
],
"weight_type": "QUInt8"
},
"decoder_model": {
"op_types": [
"Range",
"Concat",
"Expand",
"Constant",
"Sub",
"Transpose",
"Mul",
"ConstantOfShape",
"Erf",
"Add",
"Cast",
"MatMul",
"Slice",
"Less",
"Pow",
"Unsqueeze",
"Div",
"Squeeze",
"ReduceMean",
"Where",
"Gather",
"Softmax",
"Reshape",
"Equal",
"Sqrt",
"Shape"
],
"weight_type": "QInt8"
},
"decoder_with_past_model": {
"op_types": [
"Range",
"Concat",
"Expand",
"Constant",
"Sub",
"Transpose",
"Mul",
"ConstantOfShape",
"Erf",
"Add",
"Cast",
"MatMul",
"Pow",
"Unsqueeze",
"Div",
"ReduceMean",
"Where",
"Gather",
"Softmax",
"Reshape",
"Equal",
"Sqrt",
"Shape"
],
"weight_type": "QInt8"
},
"decoder_model_merged": {
"op_types": [
"Range",
"Concat",
"Expand",
"Constant",
"Sub",
"Transpose",
"Mul",
"ConstantOfShape",
"Erf",
"Add",
"Cast",
"MatMul",
"Slice",
"Less",
"If",
"Pow",
"Unsqueeze",
"Div",
"Squeeze",
"ReduceMean",
"Where",
"Gather",
"Softmax",
"Reshape",
"Equal",
"Sqrt",
"Shape"
],
"weight_type": "QInt8"
}
}
} |