Instructions to use ReasoningShield/ReasoningShield-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ReasoningShield/ReasoningShield-1B with Transformers:

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

pipe = pipeline("text-generation", model="ReasoningShield/ReasoningShield-1B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ReasoningShield/ReasoningShield-1B", dtype="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ReasoningShield/ReasoningShield-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ReasoningShield/ReasoningShield-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReasoningShield/ReasoningShield-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ReasoningShield/ReasoningShield-1B

SGLang

How to use ReasoningShield/ReasoningShield-1B 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 "ReasoningShield/ReasoningShield-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReasoningShield/ReasoningShield-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ReasoningShield/ReasoningShield-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReasoningShield/ReasoningShield-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ReasoningShield/ReasoningShield-1B with Docker Model Runner:
```
docker model run hf.co/ReasoningShield/ReasoningShield-1B
```

ReasoningShield commited on May 22, 2025

Commit

6fba110

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1 Parent(s): 78f786f

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@@ -64,7 +64,7 @@ datasets:
 ##  🛡 1. Model Overview
-***ReasoningShield*** is the first specialized safety moderation model tailored to identify hidden risks in intermediate reasoning steps in Large Reasoning Models (LRMs) before generating final answers. It excels in detecting harmful content that may be concealed within seemingly harmless reasoning traces, ensuring robust safety alignment for LRMs.
 - **Primary Use Case** : Detecting and mitigating hidden risks in reasoning traces of Large Reasoning Models (LRMs)

 ##  🛡 1. Model Overview
+***ReasoningShield*** is the first specialized safety moderation model tailored to identify hidden risks in intermediate reasoning steps in Large Reasoning Models (LRMs) before generating final answers. It excels in detecting harmful content that may be concealed within seemingly harmless reasoning traces, ensuring robust safety for LRMs.
 - **Primary Use Case** : Detecting and mitigating hidden risks in reasoning traces of Large Reasoning Models (LRMs)