Instructions to use LeonOverload/Qwen2.5-1.5B-Open-R1-Distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeonOverload/Qwen2.5-1.5B-Open-R1-Distill with Transformers:

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

pipe = pipeline("text-generation", model="LeonOverload/Qwen2.5-1.5B-Open-R1-Distill")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeonOverload/Qwen2.5-1.5B-Open-R1-Distill")
model = AutoModelForCausalLM.from_pretrained("LeonOverload/Qwen2.5-1.5B-Open-R1-Distill")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LeonOverload/Qwen2.5-1.5B-Open-R1-Distill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeonOverload/Qwen2.5-1.5B-Open-R1-Distill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeonOverload/Qwen2.5-1.5B-Open-R1-Distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeonOverload/Qwen2.5-1.5B-Open-R1-Distill

SGLang

How to use LeonOverload/Qwen2.5-1.5B-Open-R1-Distill 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 "LeonOverload/Qwen2.5-1.5B-Open-R1-Distill" \
    --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": "LeonOverload/Qwen2.5-1.5B-Open-R1-Distill",
		"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 "LeonOverload/Qwen2.5-1.5B-Open-R1-Distill" \
        --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": "LeonOverload/Qwen2.5-1.5B-Open-R1-Distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeonOverload/Qwen2.5-1.5B-Open-R1-Distill with Docker Model Runner:
```
docker model run hf.co/LeonOverload/Qwen2.5-1.5B-Open-R1-Distill
```

Qwen2.5-1.5B-Open-R1-Distill / model.safetensors

Commit History

Training in progress, step 268

a6b7693
verified

LeonOverload commited on Mar 9, 2025

Training in progress, step 200

e7f11c2
verified

LeonOverload commited on Mar 9, 2025

Training in progress, step 100

3d43840
verified

LeonOverload commited on Mar 9, 2025

Commit History

Training in progress, step 268 a6b7693 verified

Training in progress, step 200 e7f11c2 verified

Training in progress, step 100 3d43840 verified

Training in progress, step 268

a6b7693
verified

Training in progress, step 200

e7f11c2
verified

Training in progress, step 100

3d43840
verified