Text Generation
Transformers
PyTorch
bart
mass-spectrometry
GC-EI-MS
Transformer
molecular-structure-reconstruction
compound-identification
Instructions to use LMHHHHHH/SpecTUS_pretrained_only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Xet hash:
- 4adf7a6d5797ecc42932003d8cdf1e168234d605c3f6bb399dd88715732c01b6
- Size of remote file:
- 4.28 kB
- SHA256:
- e2cfe7ae37f84d703ef53d41db5ab1dd03d554c69029ddd52b7ede4d6b5a0cb4
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