Instructions to use sailing-lab/SR2AM-v0.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sailing-lab/SR2AM-v0.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="sailing-lab/SR2AM-v0.1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sailing-lab/SR2AM-v0.1-8B")
model = AutoModelForCausalLM.from_pretrained("sailing-lab/SR2AM-v0.1-8B")
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 sailing-lab/SR2AM-v0.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sailing-lab/SR2AM-v0.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sailing-lab/SR2AM-v0.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sailing-lab/SR2AM-v0.1-8B

SGLang

How to use sailing-lab/SR2AM-v0.1-8B 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 "sailing-lab/SR2AM-v0.1-8B" \
    --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": "sailing-lab/SR2AM-v0.1-8B",
		"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 "sailing-lab/SR2AM-v0.1-8B" \
        --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": "sailing-lab/SR2AM-v0.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sailing-lab/SR2AM-v0.1-8B with Docker Model Runner:
```
docker model run hf.co/sailing-lab/SR2AM-v0.1-8B
```

mingkaid commited on 1 day ago

Commit

5d72255

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1 Parent(s): 814a13d

Update README.md

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README.md +7 -5

README.md CHANGED Viewed

@@ -22,7 +22,7 @@ We argue that efficient agentic reasoning benefits from decomposing deliberation
 SR²AM-v0.1-8B achieves an overall Pass@1 of **57.0** across 11 benchmarks spanning math, science, tabular analysis, and web information seeking — competitive with systems at 120–355B parameters.
-More details: [project website](https://sailing-lab.github.io/sr2am-self-regulated-planning) | [paper](https://arxiv.org) | [GitHub](https://github.com/sailing-lab/sr2am).
 ## Key Features
@@ -40,15 +40,17 @@ See the [GitHub repository](https://github.com/sailing-lab/sr2am) for setup and
 ![Pass@1 vs. parameter size and reasoning-token count](main-results.png)
-SR²AM-v0.1-8B sits above the size-vs-accuracy trendline in (a). The full benchmark breakdown is in the [paper](https://arxiv.org).
 ## Citation
 ```bibtex
-@inproceedings{sr2am2026,
   title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
-  author={Deng, Mingkai and Hou, Jinyu and Sá Neves, Lara and Pimpalkhute, Varad and Killian, Taylor W. and Liu, Zhengzhong and Xing, Eric P.},
-  booktitle={Preprint},
   year={2026}
 }
 ```

 SR²AM-v0.1-8B achieves an overall Pass@1 of **57.0** across 11 benchmarks spanning math, science, tabular analysis, and web information seeking — competitive with systems at 120–355B parameters.
+More details: [project website](https://sailing-lab.github.io/sr2am-self-regulated-planning) | [paper](https://arxiv.org/abs/2605.22138) | [GitHub](https://github.com/sailing-lab/sr2am).
 ## Key Features
 ![Pass@1 vs. parameter size and reasoning-token count](main-results.png)
+SR²AM-v0.1-8B sits above the size-vs-accuracy trendline in (a). The full benchmark breakdown is in the [paper](https://arxiv.org/abs/2605.22138).
 ## Citation
 ```bibtex
+@article{deng2026sr2am,
   title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
+  author={Deng, Mingkai and Hou, Jinyu and Neves, Lara Sá and
+          Pimpalkhute, Varad and Killian, Taylor W. and
+          Liu, Zhengzhong and Xing, Eric P.},
+  journal={arXiv preprint arXiv:2605.22138},
   year={2026}
 }
 ```