Instructions to use cfei621/Qwen2.5-1.5B-Open-R1-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cfei621/Qwen2.5-1.5B-Open-R1-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="cfei621/Qwen2.5-1.5B-Open-R1-GRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cfei621/Qwen2.5-1.5B-Open-R1-GRPO")
model = AutoModelForCausalLM.from_pretrained("cfei621/Qwen2.5-1.5B-Open-R1-GRPO")
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

vLLM

How to use cfei621/Qwen2.5-1.5B-Open-R1-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cfei621/Qwen2.5-1.5B-Open-R1-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cfei621/Qwen2.5-1.5B-Open-R1-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cfei621/Qwen2.5-1.5B-Open-R1-GRPO

SGLang

How to use cfei621/Qwen2.5-1.5B-Open-R1-GRPO 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 "cfei621/Qwen2.5-1.5B-Open-R1-GRPO" \
    --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": "cfei621/Qwen2.5-1.5B-Open-R1-GRPO",
		"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 "cfei621/Qwen2.5-1.5B-Open-R1-GRPO" \
        --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": "cfei621/Qwen2.5-1.5B-Open-R1-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cfei621/Qwen2.5-1.5B-Open-R1-GRPO with Docker Model Runner:
```
docker model run hf.co/cfei621/Qwen2.5-1.5B-Open-R1-GRPO
```

cfei621 commited on Apr 5, 2025

Commit

75a6b8b

verified ·

1 Parent(s): d04a94b

Training in progress, step 200

Browse files

Files changed (2) hide show

model.safetensors +1 -1
training_args.bin +1 -1

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:95c24a3618ccb1c0fba04563c272e33295547053501662b8cecdfb3764012675
 size 3087467144

 version https://git-lfs.github.com/spec/v1
+oid sha256:dad072c0642a1e8362f2e818ec4344c01863a87547c248efd166f035bf41f6d0
 size 3087467144

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f390696b25a8cb57383bca2f7d3accdabda0e4f3f4d2f3eac04a516bc94860c5
 size 8184

 version https://git-lfs.github.com/spec/v1
+oid sha256:99e4e79c0f68f46354f5e9a605ec05fd2ba93e86c6b3d6079626b3809548fd00
 size 8184