Instructions to use OpenOneRec/OneRec-1.7B-pro-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenOneRec/OneRec-1.7B-pro-pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenOneRec/OneRec-1.7B-pro-pretrain") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpenOneRec/OneRec-1.7B-pro-pretrain") model = AutoModelForCausalLM.from_pretrained("OpenOneRec/OneRec-1.7B-pro-pretrain") 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 OpenOneRec/OneRec-1.7B-pro-pretrain with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenOneRec/OneRec-1.7B-pro-pretrain" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenOneRec/OneRec-1.7B-pro-pretrain", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenOneRec/OneRec-1.7B-pro-pretrain
- SGLang
How to use OpenOneRec/OneRec-1.7B-pro-pretrain 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 "OpenOneRec/OneRec-1.7B-pro-pretrain" \ --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": "OpenOneRec/OneRec-1.7B-pro-pretrain", "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 "OpenOneRec/OneRec-1.7B-pro-pretrain" \ --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": "OpenOneRec/OneRec-1.7B-pro-pretrain", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenOneRec/OneRec-1.7B-pro-pretrain with Docker Model Runner:
docker model run hf.co/OpenOneRec/OneRec-1.7B-pro-pretrain
| library_name: transformers | |
| license: apache-2.0 | |
| license_link: https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE | |
| <div align="center"> | |
| <h1>OpenOneRec</h1> | |
| <p align="center"> | |
| <strong>An Open Foundation Model and Benchmark to Accelerate Generative Recommendation</strong> | |
| </p> | |
| <p align="center"> | |
| <a href="https://huggingface.co/OpenOneRec"> | |
| <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OneRec-ffc107?color=ffc107&logoColor=white" /> | |
| </a> | |
| <a href="https://github.com/Kuaishou-OneRec/OpenOneRec"> | |
| <img alt="GitHub Code" src="https://img.shields.io/badge/GitHub-OpenOneRec-black?logo=github" /> | |
| </a> | |
| <a href="https://arxiv.org/pdf/2512.24762"> | |
| <img alt="Paper" src="https://img.shields.io/badge/Paper-ArXiv-b31b1b?logo=arxiv" /> | |
| </a> | |
| <a href="#license"> | |
| <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green" /> | |
| </a> | |
| </p> | |
| </div> | |
| <br> | |
| ## 📖 OneRec-Foundation-Pretrain Models | |
| This repository provides the pre-trained weights of the OneRec-Foundation series, which has undergone Itemic-Text Alignment and Full-Parameter Co-Pretraining. | |
| We release this checkpoint to enable users to perform customized post-training or alignment tailored to their specific downstream tasks and datasets, providing greater flexibility for specialized research. | |
| For technical details on the pre-training architecture, please refer to our Technical Report. |