Instructions to use ninty-seven/StepGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ninty-seven/StepGuard with Transformers:

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

pipe = pipeline("text-generation", model="ninty-seven/StepGuard")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ninty-seven/StepGuard")
model = AutoModelForCausalLM.from_pretrained("ninty-seven/StepGuard", 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 ninty-seven/StepGuard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ninty-seven/StepGuard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ninty-seven/StepGuard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ninty-seven/StepGuard

SGLang

How to use ninty-seven/StepGuard 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 "ninty-seven/StepGuard" \
    --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": "ninty-seven/StepGuard",
		"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 "ninty-seven/StepGuard" \
        --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": "ninty-seven/StepGuard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ninty-seven/StepGuard with Docker Model Runner:
```
docker model run hf.co/ninty-seven/StepGuard
```

ninty-seven commited on 5 days ago

Commit

0f4cf3c

verified ·

1 Parent(s): 04aa4f8

Rename model and update model card to StepGuard

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -11,9 +11,9 @@ tags:
 - step-level-safety
 ---
-# AgentDoG-Step
-**AgentDoG-Step** is a 4B guard model for step-level safety judgment in
 tool-using LLM agents. It supports both pre-execution evaluation of candidate
 tool actions and safety auditing of completed agent trajectories.
@@ -22,8 +22,8 @@ with cold-start supervised fine-tuning on data generated by **StepGen**, followe
 by **Balance-GRPO** post-training to reduce the performance gap between safe and
 unsafe decisions.
-- **Project page:** https://zheng977.github.io/AgentDoG-Step/
-- **Code:** https://github.com/zheng977/AgentDoG-Step
 ## Intended Use

 - step-level-safety
 ---
+# StepGuard
+**StepGuard** is a 4B guard model for step-level safety judgment in
 tool-using LLM agents. It supports both pre-execution evaluation of candidate
 tool actions and safety auditing of completed agent trajectories.
 by **Balance-GRPO** post-training to reduce the performance gap between safe and
 unsafe decisions.
+- **Project page:** https://zheng977.github.io/StepGuard/
+- **Code:** https://github.com/zheng977/StepGuard
 ## Intended Use