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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Joinn/UserMirrorrer-Llama-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-Llama-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

UserMirrorrer-Llama-DPO

This is a preference-aligned user simulator for recommendation systems, fine-tuned from Llama-3.2-3B-Instruct using the UserMirrorer framework.

The model was introduced in the paper Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation.

Model Details

UserMirrorer is designed to simulate user behavior and preferences in recommender systems by leveraging extensive user feedback. The framework generates decision-making processes as explanatory rationales to enhance alignment with human preferences.

The fine-tuning process involved two stages:

Supervised Fine-tuning (SFT): 1 epoch.
Direct Preference Optimization (DPO): 2 epochs.

Resources

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

Citation

If you find this work useful, please consider citing:

@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}, 
}

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Safetensors

Model size

3B params

Tensor type

F32

Model tree for Joinn/UserMirrorrer-Llama-DPO

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

(1656)

this model

Dataset used to train Joinn/UserMirrorrer-Llama-DPO

Paper for Joinn/UserMirrorrer-Llama-DPO

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation

Paper • 2508.18142 • Published Aug 25, 2025