Text Generation
Transformers
Safetensors
English
gpt2
feature-extraction
Domain-Certification
Jailbreaking
Adversarial-Attack
Guardrail
text-generation-inference
Instructions to use cemde/Domain-Certification-MedQA-Guide-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cemde/Domain-Certification-MedQA-Guide-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cemde/Domain-Certification-MedQA-Guide-Base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cemde/Domain-Certification-MedQA-Guide-Base") model = AutoModel.from_pretrained("cemde/Domain-Certification-MedQA-Guide-Base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use cemde/Domain-Certification-MedQA-Guide-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cemde/Domain-Certification-MedQA-Guide-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cemde/Domain-Certification-MedQA-Guide-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cemde/Domain-Certification-MedQA-Guide-Base
- SGLang
How to use cemde/Domain-Certification-MedQA-Guide-Base 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 "cemde/Domain-Certification-MedQA-Guide-Base" \ --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": "cemde/Domain-Certification-MedQA-Guide-Base", "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 "cemde/Domain-Certification-MedQA-Guide-Base" \ --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": "cemde/Domain-Certification-MedQA-Guide-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cemde/Domain-Certification-MedQA-Guide-Base with Docker Model Runner:
docker model run hf.co/cemde/Domain-Certification-MedQA-Guide-Base
Update model card
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README.md
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datasets:
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language:
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- Domain-Certification
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- Jailbreaking
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- Adversarial-Attack
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- Guardrail
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# Shh, don't say that! Domain Certification in LLMs
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[](https://iclr.cc/virtual/2025/poster/30364)
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[](https://github.com/cemde/Domain-Certification)
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**Certify you Large Language Model (LLM)!**
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- Domain-Certification
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- Jailbreaking
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- Adversarial-Attack
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- Guardrail
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datasets:
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- qiaojin/PubMedQA
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---
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# Shh, don't say that! Domain Certification in LLMs
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[](https://iclr.cc/virtual/2025/poster/30364)
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[](https://github.com/cemde/Domain-Certification)
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**Collection:** https://huggingface.co/collections/cemde/domain-certification-67ba4fb663f8d1348c3c2263
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**Certify you Large Language Model (LLM)!**
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