Instructions to use Xenova/OpenELM-270M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/OpenELM-270M-Instruct with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'Xenova/OpenELM-270M-Instruct');
File size: 3,525 Bytes
965fec0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | {
"fp16": {},
"q8": {
"per_model_config": {
"model": {
"op_types": [
"Add",
"And",
"Cast",
"Concat",
"Constant",
"ConstantOfShape",
"Div",
"Equal",
"Expand",
"Gather",
"MatMul",
"Mul",
"Neg",
"Pow",
"Range",
"ReduceMean",
"Reshape",
"ScatterND",
"Shape",
"Sigmoid",
"Slice",
"Softmax",
"Split",
"Sqrt",
"Sub",
"Tile",
"Transpose",
"Unsqueeze",
"Where"
],
"weight_type": "QInt8"
}
},
"per_channel": false,
"reduce_range": false
},
"int8": {
"per_model_config": {
"model": {
"op_types": [
"Add",
"And",
"Cast",
"Concat",
"Constant",
"ConstantOfShape",
"Div",
"Equal",
"Expand",
"Gather",
"MatMul",
"Mul",
"Neg",
"Pow",
"Range",
"ReduceMean",
"Reshape",
"ScatterND",
"Shape",
"Sigmoid",
"Slice",
"Softmax",
"Split",
"Sqrt",
"Sub",
"Tile",
"Transpose",
"Unsqueeze",
"Where"
],
"weight_type": "QInt8"
}
},
"per_channel": false,
"reduce_range": false
},
"uint8": {
"per_model_config": {
"model": {
"op_types": [
"Add",
"And",
"Cast",
"Concat",
"Constant",
"ConstantOfShape",
"Div",
"Equal",
"Expand",
"Gather",
"MatMul",
"Mul",
"Neg",
"Pow",
"Range",
"ReduceMean",
"Reshape",
"ScatterND",
"Shape",
"Sigmoid",
"Slice",
"Softmax",
"Split",
"Sqrt",
"Sub",
"Tile",
"Transpose",
"Unsqueeze",
"Where"
],
"weight_type": "QUInt8"
}
},
"per_channel": false,
"reduce_range": false
},
"q4": {
"block_size": 32,
"is_symmetric": true,
"accuracy_level": null
},
"bnb4": {
"block_size": 64,
"quant_type": 1
}
} |