Instructions to use Aurigene-AI/molt5-large-smiles2caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aurigene-AI/molt5-large-smiles2caption with Transformers:

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

pipe = pipeline("text-generation", model="Aurigene-AI/molt5-large-smiles2caption")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Aurigene-AI/molt5-large-smiles2caption")
model = AutoModelForSeq2SeqLM.from_pretrained("Aurigene-AI/molt5-large-smiles2caption", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Aurigene-AI/molt5-large-smiles2caption with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aurigene-AI/molt5-large-smiles2caption"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aurigene-AI/molt5-large-smiles2caption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Aurigene-AI/molt5-large-smiles2caption

SGLang

How to use Aurigene-AI/molt5-large-smiles2caption 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 "Aurigene-AI/molt5-large-smiles2caption" \
    --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": "Aurigene-AI/molt5-large-smiles2caption",
		"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 "Aurigene-AI/molt5-large-smiles2caption" \
        --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": "Aurigene-AI/molt5-large-smiles2caption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Aurigene-AI/molt5-large-smiles2caption with Docker Model Runner:
```
docker model run hf.co/Aurigene-AI/molt5-large-smiles2caption
```

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

Mirrored by Aurigene AI

Discovery stage: Evidence and literature / Lead optimization

Writes a natural-language description of a molecule from its SMILES. Useful for annotating screening hits and summarising libraries.

Upstream: laituan245/molt5-large-smiles2caption - all credit to the original authors; the model card and licence below are theirs.

Explore the rest of the catalogue: Molecule Explorer - Protein Target Explorer - Drug Discovery Model Hub

This model can be used to generate an input caption from a SMILES string.

Example Usage

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large-smiles2caption')

input_text = 'C1=CC2=C(C(=C1)[O-])NC(=CC2=O)C(=O)O'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Paper

For more information, please take a look at our paper.

Paper: Translation between Molecules and Natural Language

Authors: Carl Edwards*, Tuan Lai*, Kevin Ros, Garrett Honke, Heng Ji

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Collection including Aurigene-AI/molt5-large-smiles2caption

Generative Chemistry & Synthesis Planning

Collection

De novo molecule generation, forward reaction prediction, retrosynthesis and molecule captioning. • 5 items • Updated about 16 hours ago

Paper for Aurigene-AI/molt5-large-smiles2caption

Translation between Molecules and Natural Language

Paper • 2204.11817 • Published Apr 25, 2022