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

70c4582

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1 Parent(s): ef887dc

Update README.md

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@@ -34,11 +34,11 @@ for subsequent finetuning.
 We suggest to finetune the model further on experimental data (NIST, Wiley) to reach the performance reported in our [preprint]. Though we can not
 make the final model available, since it was finetuned on a proprietary dataset (NIST). If youhave purchased the NIST GC-EI-MS license, you can
 either fine-tune the model yourself using the code in [our GitHub repository] or contact us with a proof of the license and we will share the final
-model with you. The code we used for the data processing, finetuning, evaluation, model comparison and more can also be found in [our GitHub repository] (TODO).
-Our [preprint] (TODO) 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/
-[preprint]: !TODO!

 We suggest to finetune the model further on experimental data (NIST, Wiley) to reach the performance reported in our [preprint]. Though we can not
 make the final model available, since it was finetuned on a proprietary dataset (NIST). If youhave purchased the NIST GC-EI-MS license, you can
 either fine-tune the model yourself using the code in [our GitHub repository] or contact us with a proof of the license and we will share the final
+model with you. The code we used for the data processing, finetuning, evaluation, model comparison and more can also be found in [our GitHub repository].
+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/
+[preprint]: https://arxiv.org/abs/2502.05114