Instructions to use TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH with Transformers:

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

pipe = pipeline("text-generation", model="TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH")
model = AutoModelForCausalLM.from_pretrained("TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH

SGLang

How to use TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH 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 "TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH" \
    --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": "TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH",
		"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 "TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH" \
        --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": "TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH with Docker Model Runner:
```
docker model run hf.co/TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base-MATH
```

Self-Certainty-Qwen3-1.7B-Base-MATH / README.md

resistz

nielsr HF Staff

Improve model card: Add metadata, paper link, and correct GitHub link (#1)

b6750c6 verified about 1 year ago

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metadata

license: mit
pipeline_tag: text-generation
library_name: transformers
tags:
  - qwen3
  - fine-tuning
  - reinforcement-learning
  - reasoning
  - mathematical-reasoning
  - self-supervised-learning

Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models

TMLR-Group-HF/Self-Certainty-Qwen3-1.7B-Base

This is the Qwen3-1.7B-Base model trained by the Self-Certainty method using the MATH training set. This model serves as a baseline for comparison within the framework presented in the paper Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models.

The paper "Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models" introduces a novel self-supervised reinforcement learning framework that enhances training stability by incorporating complementary supervision signals.

For more details on the Co-rewarding framework, training procedures, and other released models and datasets, please refer to the official GitHub repository: https://github.com/tmlr-group/Co-rewarding.

Citation

If you use our datasets or models, please cite our paper!

@article{zhang2025co,
  title={Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models},
  author={Zhang, Zizhuo and Zhu, Jianing and Ge, Xinmu and Zhao, Zihua and Zhou, Zhanke and Li, Xuan and Feng, Xiao and Yao, Jiangchao and Han, Bo},
  journal={arXiv preprint arXiv:2508.00410},
  year={2025}
}