Instructions to use AbidHasan95/smsner_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbidHasan95/smsner_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AbidHasan95/smsner_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbidHasan95/smsner_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AbidHasan95/smsner_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AbidHasan95/smsner_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbidHasan95/smsner_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AbidHasan95/smsner_model
- SGLang
How to use AbidHasan95/smsner_model 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 "AbidHasan95/smsner_model" \ --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": "AbidHasan95/smsner_model", "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 "AbidHasan95/smsner_model" \ --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": "AbidHasan95/smsner_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AbidHasan95/smsner_model with Docker Model Runner:
docker model run hf.co/AbidHasan95/smsner_model
Download model3.pth from AbidHasan95/smsner_model: direct link, hf CLI and curl.
- Browser
- Download file 89.9 MB
-
https://huggingface.co/AbidHasan95/smsner_model/resolve/main/model3.pth
- Command line
-
hf download hf://AbidHasan95/smsner_model/model3.pth
-
curl -L -o model3.pth https://huggingface.co/AbidHasan95/smsner_model/resolve/main/model3.pth
89.9 MB
- Xet hash:
- 858f51ecf78930fc8438926c67be51fb047a56b0109b380450a72e4769806876
- Size of remote file:
- 89.9 MB
- SHA256:
- ae032414deee1ccbe1fb94a851f487c1ee0a39649d69c883a4814dd28f01cedc
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