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

1de3670

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1 Parent(s): 36f748e

Update README.md

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@@ -17,7 +17,7 @@ The SpecTUS model pretrained on synth2_2x4.7M for 2x112k 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 9.4M synthetic spectra generated from two identical sets of 4.7M
-compounds using the NEIMS [1] and RASSP [2] 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 224,000 steps, allowing the model to process each of the 9.4 million spectra approximately three times.
@@ -30,9 +30,9 @@ These results suggest that during the pretraining phase, the model successfully
 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 [3].
-[1]: https://github.com/brain-research/deep-molecular-massspec
-[2]: https://github.com/thejonaslab/rassp-public
-[3]: !! <TODO>

 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 9.4M synthetic spectra generated from two identical sets of 4.7M
+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 224,000 steps, allowing the model to process each of the 9.4 million spectra approximately three times.
 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!