Instructions to use HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration with Transformers:

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

pipe = pipeline("text-generation", model="HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration")
model = AutoModelForCausalLM.from_pretrained("HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration")
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 HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration

SGLang

How to use HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration 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 "HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration" \
    --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": "HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration",
		"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 "HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration" \
        --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": "HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration with Docker Model Runner:
```
docker model run hf.co/HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration
```

Model Card for Model ID

Model trained based on Qwen/Qwen2.5-7B-Instruct by Self-Calibration proposed by Efficient Test-Time Scaling via Self-Calibration.

Model Sources

Repository: https://github.com/Chengsong-Huang/Self-Calibration
Paper : Efficient Test-Time Scaling via Self-Calibration

Citation

BibTeX:

@misc{huang2025efficienttesttimescalingselfcalibration,
      title={Efficient Test-Time Scaling via Self-Calibration}, 
      author={Chengsong Huang and Langlin Huang and Jixuan Leng and Jiacheng Liu and Jiaxin Huang},
      year={2025},
      eprint={2503.00031},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2503.00031}, 
}