Instructions to use Joinn/UserMirrorrer-Qwen-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joinn/UserMirrorrer-Qwen-DPO with Transformers:

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

pipe = pipeline("text-generation", model="Joinn/UserMirrorrer-Qwen-DPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Joinn/UserMirrorrer-Qwen-DPO")
model = AutoModelForCausalLM.from_pretrained("Joinn/UserMirrorrer-Qwen-DPO")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Joinn/UserMirrorrer-Qwen-DPO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Joinn/UserMirrorrer-Qwen-DPO

SGLang

How to use Joinn/UserMirrorrer-Qwen-DPO 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 "Joinn/UserMirrorrer-Qwen-DPO" \
    --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": "Joinn/UserMirrorrer-Qwen-DPO",
		"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 "Joinn/UserMirrorrer-Qwen-DPO" \
        --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": "Joinn/UserMirrorrer-Qwen-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Joinn/UserMirrorrer-Qwen-DPO with Docker Model Runner:
```
docker model run hf.co/Joinn/UserMirrorrer-Qwen-DPO
```

UserMirrorrer-Qwen-DPO

This is a fine-tuned user simulator model introduced in the paper "Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation".

The model is designed to simulate user behavior in recommender systems (RSs) by leveraging extensive user feedback to achieve better preference alignment. It uses decision-making processes as explanatory rationales to reduce ambiguity in simulation samples.

Model Details

Base Model: Qwen-2.5-3B-Instruct
Fine-tuning Process:
1. Supervised Finetuning (SFT): 1 epoch.
2. Direct Preference Optimization (DPO): 2 epochs.
Dataset: UserMirrorer

Resources

Paper: Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
GitHub Repository: Joinn99/UserMirrorer
Training Data: UserMirrorer Training Set

Citation

If you find this work useful in your research, please consider citing the following paper:

@misc{wei2025mirroringusersbuildingpreferencealigned,
      title={Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation}, 
      author={Tianjun Wei and Huizhong Guo and Yingpeng Du and Zhu Sun and Huang Chen and Dongxia Wang and Jie Zhang},
      year={2025},
      eprint={2508.18142},
      archivePrefix={arXiv},
      primaryClass={cs.HC},
      url={https://arxiv.org/abs/2508.18142}, 
}