Text Generation
Transformers
Safetensors
English
internlm2
feature-extraction
ai-safety
safetyguard
safety
benchmark
internlm
evaluation
judge
conversational
custom_code
Instructions to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenSafetyLab/MD-Judge-v0_2-internlm2_7b
- SGLang
How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b 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 "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with Docker Model Runner:
docker model run hf.co/OpenSafetyLab/MD-Judge-v0_2-internlm2_7b
- Xet hash:
- e887ae0d6cda77422ac8282c325d987a304b874eabd58019388c68603d8cf18f
- Size of remote file:
- 1.98 GB
- SHA256:
- 8cb134debd1ca0131c90c95f64f235c1a21d12aefbc5b6ad7602459de580aecc
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