Instructions to use zenosai/MonkeyOCRv2-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenosai/MonkeyOCRv2-S with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="zenosai/MonkeyOCRv2-S", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenosai/MonkeyOCRv2-S", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 774 Bytes
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"architectures": [
"MonkeyOCRv2VisionTransformer"
],
"auto_map": {
"AutoConfig": "configuration_monkeyocrv2vit.MonkeyOCRv2VisionConfig",
"AutoModel": "modeling_monkeyocrv2_vision.MonkeyOCRv2VisionTransformer"
},
"vision_attn_implementation": "flash_attention_2",
"dtype": "bfloat16",
"embed_dim": 768,
"gradient_checkpointing": false,
"hidden_size": 1024,
"init_merger_std": 0.02,
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_causal": false,
"model_type": "monkeyocr_vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"pad_token_id": 151643,
"patch_size": 14,
"post_norm": true,
"rms_norm_eps": 1e-05,
"spatial_merge_size": 2,
"temporal_patch_size": 1,
"use_bias": false
}
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