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

78f786f

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@@ -69,7 +69,7 @@ datasets:
 - **Primary Use Case** : Detecting and mitigating hidden risks in reasoning traces of Large Reasoning Models (LRMs)
 - **Key Features** :
-  - **High Performance**:  Achieves an average F1 score exceeding **92%** in QT Moderation tasks, outperforming existing models across both in-distribution (ID) and out-of-distribution (OOD) test sets, achieving state-of-the-art (SOTA) performance.
   - **Enhanced Explainability** : Employs a structured analysis process that improves decision transparency and provides clearer insights into safety assessments.

 - **Primary Use Case** : Detecting and mitigating hidden risks in reasoning traces of Large Reasoning Models (LRMs)
 - **Key Features** :
+  - **High Performance**:  Achieves an average F1 score exceeding **92%** in QT Moderation tasks, outperforming existing models across both in-distribution (ID) and out-of-distribution (OOD) test sets, achieving **state-of-the-art (SOTA)** performance.
   - **Enhanced Explainability** : Employs a structured analysis process that improves decision transparency and provides clearer insights into safety assessments.