Instructions to use EMINEM-P/safety_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMINEM-P/safety_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EMINEM-P/safety_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EMINEM-P/safety_model") model = AutoModelForCausalLM.from_pretrained("EMINEM-P/safety_model", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EMINEM-P/safety_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EMINEM-P/safety_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EMINEM-P/safety_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EMINEM-P/safety_model
- SGLang
How to use EMINEM-P/safety_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 "EMINEM-P/safety_model" \ --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": "EMINEM-P/safety_model", "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 "EMINEM-P/safety_model" \ --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": "EMINEM-P/safety_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use EMINEM-P/safety_model with Docker Model Runner:
docker model run hf.co/EMINEM-P/safety_model
| {%- set enable_thinking = false %} | |
| {%- if messages[0]['role'] == 'system' %} | |
| {%- set system_message = messages[0]['content'] %} | |
| {%- set loop_messages = messages[1:] %} | |
| {%- else %} | |
| {%- set system_message = "You are a safety-aware AI assistant. For every question, carefully consider what is safe, ethical, and appropriate before answering.\n\nFor MULTIPLE-CHOICE questions: select the letter of the safest, most ethical answer. Your final answer MUST be written as \\boxed{A} (replacing A with the correct letter).\n\nAlways end your response with \\boxed{LETTER} on its own line." %} | |
| {%- set loop_messages = messages %} | |
| {%- endif %} | |
| <|im_start|>system | |
| {{ system_message }}<|im_end|> | |
| {%- for message in loop_messages %} | |
| <|im_start|>{{ message['role'] }} | |
| {{ message['content'] }}<|im_end|> | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| <|im_start|>assistant | |
| <think> | |
| </think> | |
| The answer is \boxed{ | |
| {%- endif %} | |