Instructions to use TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-MATH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-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-Qwen2.5-7B-MATH")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-MATH")
model = AutoModelForCausalLM.from_pretrained("TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-MATH")
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 TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-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-Qwen2.5-7B-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-Qwen2.5-7B-MATH",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-MATH

SGLang

How to use TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-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-Qwen2.5-7B-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-Qwen2.5-7B-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-Qwen2.5-7B-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-Qwen2.5-7B-MATH",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-MATH with Docker Model Runner:
```
docker model run hf.co/TMLR-Group-HF/Self-Certainty-Qwen2.5-7B-MATH
```

Improve model card: Add pipeline tag, library, paper link, and correct GitHub/Citation

by nielsr HF Staff - opened Oct 11, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+10

-7

nielsr

Oct 11, 2025

This PR significantly enhances the model card for TMLR-Group-HF/Self-Certainty-Qwen2.5-7B by:

Adding pipeline_tag: text-generation: This categorizes the model correctly for better discoverability on the Hugging Face Hub, aligning with its function for reasoning in LLMs.
Adding library_name: transformers: This indicates compatibility with the transformers library, enabling an automated inference widget and showcasing typical usage.
Linking to the official Hugging Face paper page: Providing a direct link to Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models for comprehensive paper details.
Correcting the GitHub repository link: Updating the link to the accurate project repository: https://github.com/tmlr-group/Co-rewarding.
Updating the Citation: Ensuring the citation block reflects the correct paper title and author list as found in the official GitHub repository and paper.

These changes will make the model card more informative, discoverable, and user-friendly.

Improve model card: Add pipeline tag, library, paper link, and correct GitHub/Citation8d6a04cb

Geraldxm changed pull request status to merged Oct 11, 2025

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