Instructions to use xiongpeng/TTCL-NQ-Qwen3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xiongpeng/TTCL-NQ-Qwen3-4B with Transformers:

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

pipe = pipeline("text-generation", model="xiongpeng/TTCL-NQ-Qwen3-4B")

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

tokenizer = AutoTokenizer.from_pretrained("xiongpeng/TTCL-NQ-Qwen3-4B")
model = AutoModelForCausalLM.from_pretrained("xiongpeng/TTCL-NQ-Qwen3-4B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xiongpeng/TTCL-NQ-Qwen3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xiongpeng/TTCL-NQ-Qwen3-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiongpeng/TTCL-NQ-Qwen3-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xiongpeng/TTCL-NQ-Qwen3-4B

SGLang

How to use xiongpeng/TTCL-NQ-Qwen3-4B 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 "xiongpeng/TTCL-NQ-Qwen3-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiongpeng/TTCL-NQ-Qwen3-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "xiongpeng/TTCL-NQ-Qwen3-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiongpeng/TTCL-NQ-Qwen3-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xiongpeng/TTCL-NQ-Qwen3-4B with Docker Model Runner:
```
docker model run hf.co/xiongpeng/TTCL-NQ-Qwen3-4B
```

TTCL-NQ-Qwen3-4B

Final Test-time Calibration Learning (TTCL) checkpoint for Natural Questions (NQ), adapted from Qwen3-4B. TTCL uses unlabeled target-task questions to adapt answer generation and verbalized confidence.

Starting model: Qwen/Qwen3-4B
Target benchmark: Natural Questions (NQ)
Released checkpoint: final TTCL step 120
Format: merged Hugging Face Safetensors weights

Use the tokenizer and chat template included in this repository. For experiment reproduction, keep the task-specific prompt, decoding settings, and evaluation protocol consistent with the paper.