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

1304596

verified ·

1 Parent(s): 6fba110

Update README.md

Browse files

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README.md +4 -2

README.md CHANGED Viewed

@@ -73,9 +73,9 @@ datasets:
   - **Enhanced Explainability** : Employs a structured analysis process that improves decision transparency and provides clearer insights into safety assessments.
-  - **Robust Generalization** : Demonstrates competitive performance in traditional QA Moderation tasks despite being trained exclusively on a 7K-sample QT dataset.
-  - **Efficient Design** : Built on compact 1B/3B base models, requiring only **2.30 GB/5.98 GB** GPU memory during inference, facilitating cost-effective deployment on resource-constrained devices.
 - **Base Model**:  https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct & https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
@@ -92,6 +92,8 @@ datasets:
 - The model is trained on a high-quality dataset of  7,000 QT pairs, please refer to the following link for detailed information:
 - **Risk Categories** :

   - **Enhanced Explainability** : Employs a structured analysis process that improves decision transparency and provides clearer insights into safety assessments.
+  - **Robust Generalization** :  Notably, despite being trained on our 7K QT dataset only, ***ReasoningShield*** also demonstrates competitive performance in Question-Answer (QA) moderation on traditional benchmarks, rivaling baselines trained on datasets 10 times larger, aligning with **less is more** principle.
+  - **Efficient Design** : Built on compact 1B/3B base models, it requires only **2.30 GB/5.98 GB** GPU memory during inference, facilitating cost-effective deployment on resource-constrained devices.
 - **Base Model**:  https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct & https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
 - 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** :