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: 1,010 Bytes
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"assistant_confidence_threshold": 0.4,
"assistant_lookbehind": 10,
"bos_token_id": 0,
"decoder_start_token_id": 0,
"diversity_penalty": 0.0,
"do_sample": false,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"encoder_repetition_penalty": 1.0,
"eos_token_id": 2,
"epsilon_cutoff": 0.0,
"eta_cutoff": 0.0,
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"length_penalty": 1.0,
"max_length": 256,
"min_length": 0,
"no_repeat_ngram_size": 0,
"num_assistant_tokens": 20,
"num_assistant_tokens_schedule": "constant",
"num_beam_groups": 1,
"num_beams": 1,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": 1,
"remove_invalid_values": false,
"repetition_penalty": 1.0,
"return_dict_in_generate": false,
"target_lookbehind": 10,
"temperature": 1.0,
"top_k": 50,
"top_p": 1.0,
"transformers_version": "5.4.0",
"typical_p": 1.0,
"use_cache": true
}
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