Instructions to use spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B with Transformers:

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

pipe = pipeline("text-generation", model="spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B")
model = AutoModelForCausalLM.from_pretrained("spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B", device_map="auto")
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 spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-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": "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B

SGLang

How to use spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-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 "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-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": "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-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 "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-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": "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B with Docker Model Runner:
```
docker model run hf.co/spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B
```

Benjamin-eecs commited on Jul 5, 2025

Commit

635e3a9

verified ·

1 Parent(s): cded143

feat: update bibtex in README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -25,8 +25,8 @@ This model is trained with self-play on multi-games (TicTacToe, Kuhn Poker, Simp
 @article{liu2025spiral,
   title={SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning},
   author={Liu, Bo and Guertler, Leon and Yu, Simon and Liu, Zichen and Qi, Penghui and Balcells, Daniel and Liu, Mickel and Tan, Cheston and Shi, Weiyan and Lin, Min and Lee, Wee Sun and Jaques, Natasha},
-  year={2025},
   journal={arXiv preprint arXiv:2506.24119},
   url={https://arxiv.org/abs/2506.24119}
 }
 ```

 @article{liu2025spiral,
   title={SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning},
   author={Liu, Bo and Guertler, Leon and Yu, Simon and Liu, Zichen and Qi, Penghui and Balcells, Daniel and Liu, Mickel and Tan, Cheston and Shi, Weiyan and Lin, Min and Lee, Wee Sun and Jaques, Natasha},
   journal={arXiv preprint arXiv:2506.24119},
+  year={2025},
   url={https://arxiv.org/abs/2506.24119}
 }
 ```