Text Classification
Laya
multilingual
English
Japanese
coreai
modernbert
apple-silicon
on-device
calibrated-decisions
Instructions to use mlboydaisuke/Laya-Multilingual-CoreAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use mlboydaisuke/Laya-Multilingual-CoreAI with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 4,014 Bytes
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"kind": "decision",
"format": "encoder",
"model": "convaiinnovations/laya",
"subfolder": "multilingual",
"revision": "1c5edc17a7acd8701df6fc341c0d179f1c62c982",
"variants": {
"macos": {
"path": "macos/wfp16-s256",
"sizeMB": 681,
"format": "aimodel"
},
"macos-s512": {
"path": "macos/wfp16-s512",
"sizeMB": 682,
"format": "aimodel"
},
"ios": {
"path": "ios/wfp16-s256",
"sizeMB": 680,
"format": "aimodel",
"minOS": "27.0"
},
"ios-s512": {
"path": "ios/wfp16-s512",
"sizeMB": 681,
"format": "aimodel",
"minOS": "27.0"
},
"ios-h18p": {
"path": "ios-h18p/wfp16-s256",
"sizeMB": 680,
"format": "aimodelc",
"minOS": "27.0",
"architecture": "h18p"
},
"ios-h18p-s512": {
"path": "ios-h18p/wfp16-s512",
"sizeMB": 681,
"format": "aimodelc",
"minOS": "27.0",
"architecture": "h18p"
},
"macos-fp32": {
"path": "macos/fp32-s256",
"sizeMB": 1324,
"format": "aimodel",
"reference": true
},
"macos-fp32-s512": {
"path": "macos/fp32-s512",
"sizeMB": 1325,
"format": "aimodel",
"reference": true
}
},
"host": {
"layout": "laya",
"tokenizer": "tokenizer/tokenizer.json",
"cls_id": 2,
"sep_id": 1,
"pad_id": 0,
"mask_id": 4,
"head_max_len": 256,
"option_text_tokens": 48,
"source_max_len": 1024,
"sequence": "[CLS] <type> question: <instructions> [SEP] ([MASK] <option text, <=48 tokens>)... [SEP] <state, right-truncated> [SEP]",
"readout": "token_logits at the marker positions; softmax of the raw logits -> the four act features -> act; answer = softmax(logits / T), T by bucket (type:K) first, then by type",
"temperatures": "metadata.json decision block: source_temperature [1,1,1] and the fitted calibration",
"compute_units": "gpu (a Neural Engine preference is refused: its answers fall outside the bar and vary run to run)"
},
"graph": {
"functions": {
"main": {
"inputs": {
"input_ids": {
"dtype": "int32",
"shape": [
1,
256
],
"padding": "right, PAD 0"
},
"attention_mask": {
"dtype": "int32",
"shape": [
1,
256
],
"values": "1 real token, 0 padding"
},
"qtype_onehot": {
"dtype": "float32",
"shape": [
1,
3
],
"order": [
"choice",
"score",
"noul"
]
}
},
"outputs": {
"token_logits": {
"dtype": "float32",
"shape": [
1,
256
],
"read": "at the option marker positions"
},
"pooled_cls": {
"dtype": "float32",
"shape": [
1,
768
],
"read": "input of the act function"
}
}
},
"act": {
"inputs": {
"pooled_cls": {
"dtype": "float32",
"shape": [
1,
768
]
},
"feats": {
"dtype": "float32",
"shape": [
1,
4
],
"order": [
"top1",
"top1_minus_top2",
"entropy_over_ln_max_k_2",
"max_k_2_over_255"
],
"from": "softmax of the RAW marker logits (no temperature)"
}
},
"outputs": {
"act_logits": {
"dtype": "float32",
"shape": [
1,
2
],
"class_0": "direct answer"
}
}
}
},
"windows": [
256,
512
]
}
}
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