Instructions to use p208p2002/qmst-qgg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p208p2002/qmst-qgg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="p208p2002/qmst-qgg")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("p208p2002/qmst-qgg") model = AutoModelForSeq2SeqLM.from_pretrained("p208p2002/qmst-qgg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use p208p2002/qmst-qgg with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "p208p2002/qmst-qgg" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "p208p2002/qmst-qgg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/p208p2002/qmst-qgg
- SGLang
How to use p208p2002/qmst-qgg 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 "p208p2002/qmst-qgg" \ --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": "p208p2002/qmst-qgg", "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 "p208p2002/qmst-qgg" \ --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": "p208p2002/qmst-qgg", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use p208p2002/qmst-qgg with Docker Model Runner:
docker model run hf.co/p208p2002/qmst-qgg
- Xet hash:
- c49c5008cbf2c04928734cf30284602d7606971745da85ff8ddaf07b7c0c1a19
- Size of remote file:
- 558 MB
- SHA256:
- c1b14e37222180ab6a2afc21b36c54d115783419d851bfe66d2acc9a1983adf0
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