Instructions to use knowledgator/IUPAC2SMILES-canonical-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use knowledgator/IUPAC2SMILES-canonical-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="knowledgator/IUPAC2SMILES-canonical-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("knowledgator/IUPAC2SMILES-canonical-base")
model = AutoModelForSeq2SeqLM.from_pretrained("knowledgator/IUPAC2SMILES-canonical-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use knowledgator/IUPAC2SMILES-canonical-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "knowledgator/IUPAC2SMILES-canonical-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "knowledgator/IUPAC2SMILES-canonical-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/knowledgator/IUPAC2SMILES-canonical-base

SGLang

How to use knowledgator/IUPAC2SMILES-canonical-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "knowledgator/IUPAC2SMILES-canonical-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "knowledgator/IUPAC2SMILES-canonical-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "knowledgator/IUPAC2SMILES-canonical-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "knowledgator/IUPAC2SMILES-canonical-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use knowledgator/IUPAC2SMILES-canonical-base with Docker Model Runner:
```
docker model run hf.co/knowledgator/IUPAC2SMILES-canonical-base
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

IUPAC2SMILES-canonical-base

IUPAC2SMILES-canonical-base was designed to accurately translate IUPAC chemical names to SMILES.

Model Details

Model Description

IUPAC2SMILES-canonical-base is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder.

Developed by: Knowladgator Engineering
Model type: Encoder-Decoder with attention mechanism
Language(s) (NLP): SMILES, IUPAC (English)
License: Apache License 2.0

Model Sources

Paper: coming soon
Demo: ChemicalConverters

Quickstart

Firstly, install the library:

pip install chemical-converters

IUPAC to SMILES

To perform simple translation, follow the example:

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
print(converter.iupac_to_smiles('ethanol'))
print(converter.iupac_to_smiles(['ethanol', 'ethanol', 'ethanol']))

['CCO']
['CCO', 'CCO', 'CCO']

Processing in batches:

from chemicalconverters import NamesConverter

converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
print(converter.iupac_to_smiles(["buta-1,3-diene" for _ in range(10)], num_beams=1, 
                                process_in_batch=True, batch_size=1000))

['<SYST>C=CC=C', '<SYST>C=CC=C'...]

Our models also predict IUPAC styles from the table:

Style Token	Description
`<BASE>`	The most known name of the substance, sometimes is the mixture of traditional and systematic style
`<SYST>`	The totally systematic style without trivial names
`<TRAD>`	The style is based on trivial names of the parts of substances

Bias, Risks, and Limitations

This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.

Training Procedure

The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch_size=512 for 2 epochs.

Evaluation

Model	Accuracy	BLEU-4 score	Size(MB)
IUPAC2SMILES-canonical-small	88.9%	0.966	23
IUPAC2SMILES-canonical-base	93.7%	0.974	180
STOUT V2.0*	68.47%	0.92	128
*According to the original paper https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00512-4

Citation

Coming soon.

Model Card Authors

Mykhailo Shtopko

Model Card Contact

info@knowledgator.com

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Chemical Converters

Collection

Collection of models for converting chemical formats between each other. • 6 items • Updated Jan 29 • 2