Instructions to use qihoo360/Light-R1-7B-DS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qihoo360/Light-R1-7B-DS with Transformers:

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

pipe = pipeline("text-generation", model="qihoo360/Light-R1-7B-DS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qihoo360/Light-R1-7B-DS")
model = AutoModelForCausalLM.from_pretrained("qihoo360/Light-R1-7B-DS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use qihoo360/Light-R1-7B-DS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qihoo360/Light-R1-7B-DS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qihoo360/Light-R1-7B-DS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qihoo360/Light-R1-7B-DS

SGLang

How to use qihoo360/Light-R1-7B-DS 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 "qihoo360/Light-R1-7B-DS" \
    --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": "qihoo360/Light-R1-7B-DS",
		"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 "qihoo360/Light-R1-7B-DS" \
        --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": "qihoo360/Light-R1-7B-DS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qihoo360/Light-R1-7B-DS with Docker Model Runner:
```
docker model run hf.co/qihoo360/Light-R1-7B-DS
```

zhs12

nielsr HF Staff commited on Mar 17, 2025

Commit

06c9338

verified ·

1 Parent(s): 44e3134

Add pipeline tag and library name (#1)

Browse files

- Add pipeline tag and library name (58acf4b7ba35097efbdaaf61aad19d628ea8454b)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -1,7 +1,9 @@
 ---
-license: apache-2.0
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 ---
 # Light-R1-7B-DS: SOTA 7B Math Model with only 3K Data
@@ -47,4 +49,4 @@ Light-R1 did thorough decontamination with exact matching (excluding digits) and
       archivePrefix={},
       url={https://github.com/Qihoo360/Light-R1},
 }
-```

 ---
 base_model:
 - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # Light-R1-7B-DS: SOTA 7B Math Model with only 3K Data
       archivePrefix={},
       url={https://github.com/Qihoo360/Light-R1},
 }
+```