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 Dec 2, 2024

Commit

8a6e0f3

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@@ -29,10 +29,13 @@ strings (RASSP, NEIMS) were valid canonical molecules, with 83\% (RASSP), 65\% (
 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.
-The full code we used for the data processing, finetuning, evaluation model comparison and more can be found in [our GitHub repository].
 [NEIMS]: https://github.com/brain-research/deep-molecular-massspec
 [RASSP]: https://github.com/thejonaslab/rassp-public
-[our GitHub repository]: !TODO!

 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), you can fine-tune it yourself if you have purchased the license.
+The full code we used for the data processing, finetuning, evaluation, model comparison and more can 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]: !TODO!
+[preprint]: !TODO!