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
```

SR2AM-v0.1-8B / README.md

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- agent
	- reasoning
	- tool-use
	- simulative-planning
	base_model: Qwen/Qwen3-8B
	---

	# SR²AM-v0.1-8B

	![SR²AM Illustration](model.png)

	We argue that efficient agentic reasoning benefits from decomposing deliberation into three interacting systems: reactive execution (System I) for fine-grained reasoning and direct action; simulative reasoning (System II) that predicts consequences of proposed actions through a world model; and self-regulation (System III) that decides when and how deeply to plan through a learned configurator.

	SR²AM (Self-Regulated Simulative Reasoning Agentic LLM) is our instantiation: the configurator and simulative planner are realized as distinct stages within an LLM's chain-of-thought reasoning, with the LLM itself serving as the world model in language space.

	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

	- System I + II + III decomposition: a configurator (System III) decides per-turn whether to plan, continue an existing plan, or act directly; a simulative planner (System II) constructs plans grounded in predicted future states; reactive execution (System I) handles fine-grained reasoning and tool use.
	- SFT + RL training: supervised learning on data encoding the self-regulated planning structure, followed by reinforcement learning (GRPO) for task success.
	- Agentic tool use: web search (SerpAPI), web browsing with LLM summarization, and stateless Python code execution (SandboxFusion).
	- Compact and efficient: 3,698 reasoning tokens per trajectory on average — fewer or comparable to other systems at the same scale while outperforming them in Pass@1.

	## Quick Start

	See the [GitHub repository](https://github.com/sailing-lab/sr2am) for setup and inference instructions.

	## Main Results

	![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}
	}
	```

	## License

	Released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).