Instructions to use MS-ML/SpecTUS_pretrained_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MS-ML/SpecTUS_pretrained_only with Transformers:

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

pipe = pipeline("text-generation", model="MS-ML/SpecTUS_pretrained_only")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MS-ML/SpecTUS_pretrained_only")
model = AutoModelForSeq2SeqLM.from_pretrained("MS-ML/SpecTUS_pretrained_only")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MS-ML/SpecTUS_pretrained_only with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MS-ML/SpecTUS_pretrained_only

SGLang

How to use MS-ML/SpecTUS_pretrained_only 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 "MS-ML/SpecTUS_pretrained_only" \
    --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": "MS-ML/SpecTUS_pretrained_only",
		"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 "MS-ML/SpecTUS_pretrained_only" \
        --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": "MS-ML/SpecTUS_pretrained_only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MS-ML/SpecTUS_pretrained_only with Docker Model Runner:
```
docker model run hf.co/MS-ML/SpecTUS_pretrained_only
```

hajekad commited on Sep 19, 2025

Commit

f762708

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1 Parent(s): 70c4582

Update README.md

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@@ -38,6 +38,19 @@ model with you. The code we used for the data processing, finetuning, evaluation
 Our [preprint] provides more information about the task background, the final finetuned model, and the experiments.
 [NEIMS]: https://github.com/brain-research/deep-molecular-massspec
 [RASSP]: https://github.com/thejonaslab/rassp-public
 [our GitHub repository]: https://github.com/hejjack/SpecTUS/

 Our [preprint] provides more information about the task background, the final finetuned model, and the experiments.
+How to cite:
+```text
+@misc{hájek2025spectusspectraltranslatorunknown,
+      title={SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra},
+      author={Adam Hájek and Helge Hecht and Elliott J. Price and Aleš Křenek},
+      year={2025},
+      eprint={2502.05114},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2502.05114},
+}
+```
 [NEIMS]: https://github.com/brain-research/deep-molecular-massspec
 [RASSP]: https://github.com/thejonaslab/rassp-public
 [our GitHub repository]: https://github.com/hejjack/SpecTUS/