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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("UniParser/MolParser-Mobile", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from UniParser/MolParser-Mobile: direct link, hf CLI and curl.
- Browser
- Download file 173 Bytes
-
https://huggingface.co/UniParser/MolParser-Mobile/resolve/main/processor_config.json
- Command line
-
hf download hf://UniParser/MolParser-Mobile/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/UniParser/MolParser-Mobile/resolve/main/processor_config.json
173 Bytes
| { | |
| "processor_class": "MolParserProcessor", | |
| "model_name": "MolParser Mobile", | |
| "auto_map": { | |
| "AutoProcessor": "processing_molparser_mobile.MolParserProcessor" | |
| } | |
| } | |