Image-to-Text
Transformers
Safetensors
molparser_vision_encoder_decoder
image-text-to-text
chemistry
custom_code
Instructions to use UniParser/MolParser-Mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UniParser/MolParser-Mobile with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="UniParser/MolParser-Mobile", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("UniParser/MolParser-Mobile", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,720 Bytes
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"architectures": [
"MolParserVisionEncoderDecoderModel"
],
"auto_map": {
"AutoConfig": "modeling_molparser_mobile.MolParserVisionEncoderDecoderConfig",
"AutoModelForImageTextToText": "modeling_molparser_mobile.MolParserVisionEncoderDecoderModel"
},
"decoder": {
"_name_or_path": "",
"activation_dropout": 0.0,
"activation_function": "gelu",
"add_cross_attention": true,
"architectures": null,
"attention_dropout": 0.0,
"bos_token_id": 0,
"chunk_size_feed_forward": 0,
"classifier_dropout": 0.0,
"d_model": 192,
"decoder_attention_heads": 4,
"decoder_ffn_dim": 768,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"dtype": "float16",
"encoder_attention_heads": 4,
"encoder_ffn_dim": 768,
"encoder_hidden_size": 192,
"encoder_layerdrop": 0.0,
"encoder_layers": 0,
"eos_token_id": 2,
"forced_eos_token_id": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_decoder": true,
"is_encoder_decoder": false,
"label2id": {
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"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"output_attentions": false,
"output_hidden_states": false,
"pad_token_id": 1,
"problem_type": null,
"return_dict": true,
"scale_embedding": false,
"tie_word_embeddings": true,
"use_cache": true,
"vocab_size": 385
},
"decoder_start_token_id": 0,
"dtype": "float16",
"encoder": {
"_name_or_path": "",
"architectures": null,
"auto_map": {
"AutoConfig": "modeling_molparser_mobile.CustomEncoderConfig",
"AutoModel": "modeling_molparser_mobile.CustomTimmEncoder"
},
"chunk_size_feed_forward": 0,
"dtype": "float16",
"hidden_size": 192,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"initializer_range": 0.02,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"model_input_size": 224,
"model_type": "custom_timm_encoder",
"output_attentions": false,
"output_hidden_states": false,
"pixel_unshuffle": 1,
"problem_type": null,
"return_dict": true,
"timm_model_name": "vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k",
"timm_output_dim": 192,
"timm_pretrained": false
},
"is_encoder_decoder": true,
"model_type": "molparser_vision_encoder_decoder",
"pad_token_id": 1,
"tie_word_embeddings": false,
"transformers_version": "5.4.0",
"use_cache": true,
"vocab_size": 385,
"model_name": "MolParser Mobile",
"torch_dtype": "float16"
}
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