Image Feature Extraction
Transformers
Safetensors
monkeyocrv2_vitae_encoder
feature-extraction
custom_code
Instructions to use zenosai/MonkeyOCRv2-AS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenosai/MonkeyOCRv2-AS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="zenosai/MonkeyOCRv2-AS", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenosai/MonkeyOCRv2-AS", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,338 Bytes
bb5b74a | 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 | {
"architectures": [
"MonkeyOCRv2ViTAEVisionTransformer"
],
"auto_map": {
"AutoConfig": "configuration_monkeyocrv2_vitae.MonkeyOCRv2ViTAEEncoderConfig",
"AutoModel": "modeling_monkeyocrv2_vitae_vision.MonkeyOCRv2ViTAEVisionTransformer"
},
"model_type": "monkeyocrv2_vitae_encoder",
"num_channels": 3,
"patch_size": 32,
"temporal_patch_size": 1,
"stage_dims": [
64,
128,
256,
512
],
"stage_depths": [
2,
2,
8,
2
],
"stage_heads": [
1,
2,
4,
8
],
"window_size": 7,
"mlp_ratio": 4.0,
"hidden_size": 1024,
"rms_norm_eps": 1e-05,
"use_bias": false,
"attn_implementation": "sdpa",
"init_merger_std": 0.02,
"initializer_range": 0.02,
"is_causal": false,
"post_norm": true,
"gradient_checkpointing": false,
"nc_groups": [
1,
32,
64,
128
],
"rc_groups": [
1,
16,
32,
64
],
"prm_embed_dim": 64,
"downsample_ratios": [
4,
2,
2,
2
],
"kernel_sizes": [
7,
3,
3,
3
],
"rc_tokens_type": [
"window",
"window",
"transformer",
"transformer"
],
"nc_tokens_type": [
"window",
"window",
"transformer",
"transformer"
],
"rc_heads": [
1,
1,
2,
4
],
"rc_embed_dims": [
64,
64,
128,
256
]
}
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