Instructions to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with Transformers:

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

pipe = pipeline("text-generation", model="OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenSafetyLab/MD-Judge-v0_2-internlm2_7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenSafetyLab/MD-Judge-v0_2-internlm2_7b

SGLang

How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b 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 "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenSafetyLab/MD-Judge-v0_2-internlm2_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenSafetyLab/MD-Judge-v0_2-internlm2_7b with Docker Model Runner:
```
docker model run hf.co/OpenSafetyLab/MD-Judge-v0_2-internlm2_7b
```

Foreshhh commited on Mar 8, 2025

Commit

f5a3fa9

1 Parent(s): be06d12

add chattemplate in tokenizer_config.json

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -66,7 +66,7 @@ Compare our this model with other models on different public safety testsets usi
 Since we have added the chat_template in `tokenzier_config.json`, you can direct use our model without complicated chat_template.
-Here is the [VLLM](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html) usage examle:
 ```python
 from transformers import AutoTokenizer
@@ -88,7 +88,7 @@ output = llm.generate(prompt, sampling_params=SamplingParams(max_tokens=256))
 print(output[0]['outputs'][0].text.strip())
 ```
-Here is the [Transformer](https://github.com/huggingface/transformers) usage examle:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM

 Since we have added the chat_template in `tokenzier_config.json`, you can direct use our model without complicated chat_template.
+Here is the [VLLM](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html) usage example:
 ```python
 from transformers import AutoTokenizer
 print(output[0]['outputs'][0].text.strip())
 ```
+Here is the [Transformer](https://github.com/huggingface/transformers) usage example:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM