Instructions to use thu-coai/ShieldLM-14B-qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thu-coai/ShieldLM-14B-qwen with Transformers:

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

pipe = pipeline("text-generation", model="thu-coai/ShieldLM-14B-qwen", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("thu-coai/ShieldLM-14B-qwen", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thu-coai/ShieldLM-14B-qwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thu-coai/ShieldLM-14B-qwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thu-coai/ShieldLM-14B-qwen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thu-coai/ShieldLM-14B-qwen

SGLang

How to use thu-coai/ShieldLM-14B-qwen 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 "thu-coai/ShieldLM-14B-qwen" \
    --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": "thu-coai/ShieldLM-14B-qwen",
		"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 "thu-coai/ShieldLM-14B-qwen" \
        --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": "thu-coai/ShieldLM-14B-qwen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thu-coai/ShieldLM-14B-qwen with Docker Model Runner:
```
docker model run hf.co/thu-coai/ShieldLM-14B-qwen
```

ShieldLM-14B-qwen / model-00006-of-00006.safetensors

nonstopfor

Upload folder using huggingface_hub

4939c1c verified over 2 years ago

Download

History Blame Contribute Delete

3.73 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: e2fa4d8654577657b10ec5c7cfd64a50c431f6cc023e9374dd21d5b944295ace
Size of remote file:: 3.73 GB
SHA256:: 4896e003c6fac1460ae2f3c556717150fe0af5b7c9d041128b4cf0b5ca7bc03b

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.