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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

NIST license; published paper

by krenek - opened 16 days ago

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

-11

Files changed (1) hide show

README.md +9 -11

README.md CHANGED Viewed

@@ -32,22 +32,20 @@ successfully learned molecular structure rules and the relationship between atom
 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].
 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},
 }
 ```

 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 you have purchased the NIST GC-EI-MS license, you can
+ fine-tune the model yourself using the code in [our GitHub repository]. 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.
 How to cite:
 ```text
+@article{doi:10.1021/acs.analchem.6c02423,
+  author = {Hájek, Adam and Starý, Michal and Price, Elliott and Jozefov, Filip and Hecht, Helge and Křenek, Aleš},
+  title = {SpecTUS: Spectral Translator for Unknown Structures Annotation from EI-MS Spectra},
+  journal = {Analytical Chemistry},
+  doi = {10.1021/acs.analchem.6c02423},
+  note ={PMID: 42441797},
+  url = {https://doi.org/10.1021/acs.analchem.6c02423},
 }
 ```