Text Generation
Transformers
PyTorch
bart
mass-spectrometry
GC-EI-MS
Transformer
molecular-structure-reconstruction
compound-identification
Instructions to use MS-ML/SpecTUS_pretrained_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# pip install -U transformers accelerate # 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
Commit History
-
f7b765a60ed1ae4c57ca2a62d4f3e07633c1db3291e9c204e304554f3f8fae98
Verified signature3a718cc -
e700d8bc6bc4e2ec84c9798ff5592818157ff29d47bf7b6f57e26e91637b587f
Verified signature596ebc1
-
Update README.md
Verified signature8a6e0f3 -
Update README.md
Verified signatureaab53be -
Update README.md
Verified signature1de3670 -
Update README.md
Verified signature36f748e -
Update README.md
Verified signature7d1b679 -
Upload folder using huggingface_hub (#1)
Verified signature- f7c71e184da0ff2c39d2196301bf486e5b530e2858432a60e11d8fa06ebedeb8 (4a66e13d1ffc61bb3b096a9aca648e12b991a1d1) - 4e36b233e6cd1fc10173db0b3398df93831ed280fe6cdec78dc3215d7e777444 (1a2f15c79038c813392c51dc346237990510c190)b333ffd -
Update README.md
Verified signaturefb00795 -
initial commit
Verified signature9e19f4e