Instructions to use cs-552-2026-barn/safety_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cs-552-2026-barn/safety_model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cs-552-2026-barn/safety_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-barn/safety_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-barn/safety_model")
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 cs-552-2026-barn/safety_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cs-552-2026-barn/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": "cs-552-2026-barn/safety_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cs-552-2026-barn/safety_model

SGLang

How to use cs-552-2026-barn/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 "cs-552-2026-barn/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": "cs-552-2026-barn/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 "cs-552-2026-barn/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": "cs-552-2026-barn/safety_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cs-552-2026-barn/safety_model with Docker Model Runner:
```
docker model run hf.co/cs-552-2026-barn/safety_model
```

safety_model

Commit History

Automated MNLP evaluation report (2026-05-20) (#1)

6b3e637

Nahush-27

zechen-nlp commited on 15 days ago

push fine-tuned safety model

dab9a4b
verified

angana1 commited on 16 days ago

push fine-tuned safety model

07dd35f
verified

angana1 commited on 18 days ago

push fine-tuned safety model

fdce5be
verified

angana1 commited on 18 days ago

push base model checkpoint for safety

384dd69
verified

angana1 commited on 20 days ago

initial commit

b96d987
verified

angana1 commited on 25 days ago

Commit History

Automated MNLP evaluation report (2026-05-20) (#1) 6b3e637

push fine-tuned safety model dab9a4b verified

push fine-tuned safety model 07dd35f verified

push fine-tuned safety model fdce5be verified

push base model checkpoint for safety 384dd69 verified