R^2ec: Towards Large Recommender Models with Reasoning
Paper • 2505.16994 • Published • 2
How to use dd101bb/gemma-2b-instruments with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="dd101bb/gemma-2b-instruments") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dd101bb/gemma-2b-instruments", dtype="auto")How to use dd101bb/gemma-2b-instruments with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dd101bb/gemma-2b-instruments"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "dd101bb/gemma-2b-instruments",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/dd101bb/gemma-2b-instruments
How to use dd101bb/gemma-2b-instruments with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "dd101bb/gemma-2b-instruments" \
--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": "dd101bb/gemma-2b-instruments",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "dd101bb/gemma-2b-instruments" \
--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": "dd101bb/gemma-2b-instruments",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use dd101bb/gemma-2b-instruments with Docker Model Runner:
docker model run hf.co/dd101bb/gemma-2b-instruments
R2ec is a large recommender model with reasoning, generating both natural language rationales and ranked item predictions. The model is fine-tuned with reinforcement learning to enhance its reasoning capabilities for more effective recommendations.
This code repository is licensed under the MIT License. The use of R2ec models is also subject to the MIT License. R2ec series support commercial use and distillation.
@misc{you2025r2ec,
title={$\text{R}^2\text{ec}$: Towards Large Recommender Models with Reasoning},
author={Runyang You and Yongqi Li and Xinyu Lin and Xin Zhang and Wenjie Wang and Wenjie Li and Liqiang Nie},
year={2025},
eprint={2505.16994},
archivePrefix={arXiv},
primaryClass={cs.IR}
}