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

Libraries

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

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

pipe = pipeline("text-generation", model="cs-552-2026-MMRF/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-MMRF/safety_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-MMRF/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 cs-552-2026-MMRF/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-MMRF/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-MMRF/safety_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

safety_model / README.md

dremathis

End of training

c234d5b verified 2 months ago

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metadata

library_name: transformers
base_model: cs-552-2026-MMRF/15kDPO
tags:
  - generated_from_trainer
model-index:
  - name: 2xwildguard
    results: []

2xwildguard

This model is a fine-tuned version of cs-552-2026-MMRF/15kDPO on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0466

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.0856	0.0714	50	0.0800
0.0673	0.1428	100	0.0686
0.0722	0.2142	150	0.0625
0.0506	0.2856	200	0.0613
0.0480	0.3570	250	0.0549
0.0426	0.4283	300	0.0536
0.0520	0.4997	350	0.0523
0.0446	0.5711	400	0.0502
0.0586	0.6425	450	0.0477
0.0502	0.7139	500	0.0477
0.0474	0.7853	550	0.0475
0.0456	0.8567	600	0.0471
0.0491	0.9281	650	0.0466
0.0563	0.9995	700	0.0466
0.0563	1.0	701	0.0466

Framework versions

Transformers 5.7.0
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2