Instructions to use SUSTech/Qwen3-8B-CalibSFT-RLCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SUSTech/Qwen3-8B-CalibSFT-RLCR with Transformers:

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

pipe = pipeline("text-generation", model="SUSTech/Qwen3-8B-CalibSFT-RLCR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SUSTech/Qwen3-8B-CalibSFT-RLCR")
model = AutoModelForCausalLM.from_pretrained("SUSTech/Qwen3-8B-CalibSFT-RLCR", 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 SUSTech/Qwen3-8B-CalibSFT-RLCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SUSTech/Qwen3-8B-CalibSFT-RLCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SUSTech/Qwen3-8B-CalibSFT-RLCR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SUSTech/Qwen3-8B-CalibSFT-RLCR

SGLang

How to use SUSTech/Qwen3-8B-CalibSFT-RLCR 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 "SUSTech/Qwen3-8B-CalibSFT-RLCR" \
    --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": "SUSTech/Qwen3-8B-CalibSFT-RLCR",
		"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 "SUSTech/Qwen3-8B-CalibSFT-RLCR" \
        --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": "SUSTech/Qwen3-8B-CalibSFT-RLCR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SUSTech/Qwen3-8B-CalibSFT-RLCR with Docker Model Runner:
```
docker model run hf.co/SUSTech/Qwen3-8B-CalibSFT-RLCR
```

Qwen3-8B-CalibSFT-RLCR

Introduction

Qwen3-8B-CalibSFT-RLCR is Qwen3-8B-CalibSFT further trained with RLCR, a confidence-aware reinforcement learning method. It is released with our paper On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models. Compared with RLCR trained from Qwen3-8B, it improves accuracy, discrimination, and calibration on both in-distribution and out-of-distribution benchmarks. For training details, please refer to our GitHub repository.

Usage

Use the system prompt and user format from training:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SUSTech/Qwen3-8B-CalibSFT-RLCR"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")

SYSTEM_PROMPT = (
    "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. "
    "The assistant first thinks about the reasoning process in the mind and analyzes its confidence about "
    "the solution and then provides the user with the final answer as well as its confidence level. "
    "The confidence level indicates how certain the Assistant is about its answer, expressed as a decimal "
    "between 0 and 1 with exactly two decimal places, enclosed within <confidence> </confidence> tags. "
    "The response must strictly follow this format: <think> reasoning process here </think> "
    "<answer> final short answer only </answer> <confidence> 0.xx </confidence>. "
    "The <answer> tag must contain only the final answer string needed for exact-match evaluation, "
    "not a full sentence, explanation, or reasoning."
)
question = "What is the smallest positive integer n such that n^2 + n is divisible by 12?"
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": f"\n\nPROBLEM: {question}\n\n"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=16384, do_sample=True, temperature=0.6, top_p=0.95, top_k=20)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
# <think> ... </think> <answer> ... </answer> <confidence> 0.xx </confidence>

Evaluation

Results are averaged over 8 in-distribution math benchmarks (DeepScaleR-Eval, MATH-500, MinervaMath, OlympiadBench, GSM8K, AIME 2024–2026) and 8 out-of-distribution benchmarks (HotpotQA, TriviaQA, DROP, MuSiQue, LiveBenchReasoning, NQOpen, PopQA, WebQuestions), sampling at temperature 0.6 with 4 responses per question (32 on AIME). DeepScaleR-Eval consists of 2,000 DeepScaleR questions held out from training.

Model	In-distribution				Out-of-distribution
Model	Pass@1 ↑	AUROC ↑	Brier ↓	ECE ↓	Pass@1 ↑	AUROC ↑	Brier ↓	ECE ↓
Qwen3-8B	59.32	71.90	33.20	34.36	45.36	65.71	45.15	46.94
Qwen3-8B-RLCR	65.33	82.36	14.43	14.46	43.96	69.38	31.14	31.17
Qwen3-8B-CalibSFT-RLCR	67.20	86.32	12.77	8.59	45.71	71.31	23.24	18.20

Citation

@article{wang2026pitfalls,
  title={On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models},
  author={Wang, Shuoyuan and Luo, Beier and Zeng, Hao and Yu, Chengyao and Zhang, Songxin and Xie, Zejian and Jing, Bingyi and Wei, Hongxin},
  journal={arXiv preprint arXiv:2609.32470},
  year={2026}
}