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

9307c04

verified ·

1 Parent(s): 1304596

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -7

README.md CHANGED Viewed

@@ -32,11 +32,6 @@ datasets:
   <a href="https://github.com/CosmosYi/ReasoningShield" target="_blank" style="margin: 2px;">
     <img alt="GitHub Page" src="https://img.shields.io/badge/GitHub-Page-black?logo=github " style="display: inline-block; vertical-align: middle;">
   </a>
-  <!-- Paper -->
-  <a href="  " target="_blank" style="margin: 2px;">
-    <img alt="Paper" src="https://img.shields.io/badge/%E2%9C%8D%EF%B8%8F%20Paper-arXiv%202508.0001-f5de53?color=f5de53&logoColor=white " style="display: inline-block; vertical-align: middle;"/>
-  </a>
   <!-- Huggingface Model -->
   <a href="https://huggingface.co/ReasoningShield/ReasoningShield-1B" target="_blank" style="margin: 2px;">
@@ -92,8 +87,7 @@ datasets:
 - The model is trained on a high-quality dataset of  7,000 QT pairs, please refer to the following link for detailed information:
-  - https://huggingface.co/datasets/ReasoningShield/ReasoningShield-Dataset
-  -
 - **Risk Categories** :

   <a href="https://github.com/CosmosYi/ReasoningShield" target="_blank" style="margin: 2px;">
     <img alt="GitHub Page" src="https://img.shields.io/badge/GitHub-Page-black?logo=github " style="display: inline-block; vertical-align: middle;">
   </a>
   <!-- Huggingface Model -->
   <a href="https://huggingface.co/ReasoningShield/ReasoningShield-1B" target="_blank" style="margin: 2px;">
 - The model is trained on a high-quality dataset of  7,000 QT pairs, please refer to the following link for detailed information:
+  - ***ReasoningShield-Dataset:*** https://huggingface.co/datasets/ReasoningShield/ReasoningShield-Dataset
 - **Risk Categories** :