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

Libraries

How to use LMHHHHHH/SpecTUS_pretrained_only with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("LMHHHHHH/SpecTUS_pretrained_only")
model = AutoModelForSeq2SeqLM.from_pretrained("LMHHHHHH/SpecTUS_pretrained_only")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LMHHHHHH/SpecTUS_pretrained_only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LMHHHHHH/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": "LMHHHHHH/SpecTUS_pretrained_only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LMHHHHHH/SpecTUS_pretrained_only

SGLang

How to use LMHHHHHH/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 "LMHHHHHH/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": "LMHHHHHH/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 "LMHHHHHH/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": "LMHHHHHH/SpecTUS_pretrained_only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

SpecTUS_pretrained_only / README.md

LMHHHHHH

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	---
	license: cc-by-4.0
	datasets:
	- MS-ML/synth1_2x4.7M
	- MS-ML/synth2_2x4.8M
	metrics:
	- accuracy
	library_name: transformers
	tags:
	- mass-spectrometry
	- GC-EI-MS
	- Transformer
	- molecular-structure-reconstruction
	- compound-identification
	---

	The SpecTUS model pretrained on synth1_2x4.7 and synth2_2x4.8M combined for 448k steps.

	The model is a Transformer-based neural network trained to elucidate molecular structures from GC-EI-MS spectra.
	The model was pretrained on a large dataset of 17.2M synthetic training spectra generated from two identical sets of 8.6M
	compounds using the [NEIMS] and [RASSP] models.

	We mainly aimed to give the model an understanding of the chemical space of small molecules. The training was
	conducted with a batch size of 128 for 448,000 steps, allowing the model to process each of the 17.2 million spectra approximately three times.
	The entire pretraining process, including control evaluations every 16,000 steps, took 58 hours on a single Nvidia H100 GPU.

	During pretraining, the percentage of correctly reconstructed structures increased steadily but it remained relatively low at the
	end of the stage: 38% for RASSP-generated spectra, 29% for NEIMS-generated spectra, and 3% for NIST spectra. However, 96% of
	the generated SMILES strings (RASSP, NEIMS) were valid canonical molecules, with 91% (RASSP), 78% (NEIMS), and 14% (NIST) having
	correct molecular formulas, though possibly incorrect structures. These results suggest that during the pretraining phase, the model
	successfully learned molecular structure rules and the relationship between atomic weight and m/z values, forming a good foundation
	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},
	}
	```

	[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