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README.md

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----
-license: apache-2.0
----
+<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/Onerec">
+        <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/OnerecLM/OpenOneRec">
+        <img alt="GitHub Code" src="https://img.shields.io/badge/GitHub-OpenOneRec-black?logo=github" />
+    </a>
+     <a href="">
+        <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>
+
+## 📖 Introduction
+
+**OpenOneRec** is an open-source framework designed to bridge the gap between traditional recommendation systems and Large Language Models (LLMs). While Generative Recommendation has shown promise, existing models often struggle with isolated data silos and a lack of reasoning capabilities.
+
+To address this, we introduce a unified framework that comprises:
+* **RecIF-Bench**: The first holistic Recommendation Instruction-Following Benchmark, containing **100M interactions** from 200k users across heterogeneous domains (Short Video, Ads, Goods).
+* **OpenOneRec-Foundation Models**: A family of models (1.7B & 8B) built on the Qwen backbone. These models are trained on hundreds of billions of tokens, integrating collaborative signals with general semantics.
+* **Full-Stack Pipeline**: We open-source our comprehensive training pipeline, including data processing, co-pretraining, and post-training, to ensure full reproducibility and facilitate scaling law research in recommendation.
+
+## 🔥 News
+
+* **[2025.xx.xx]** 🎉 **OpenOneRec-Foundation** models (1.7B, 8B) are now available on Hugging Face!
+* **[2025.xx.xx]** 📑 The technical report [OpenOneRec Technical Report] has been released.
+* **[2025.xx.xx]** 🚀 **RecIF-Bench** dataset and evaluation scripts are open-sourced.
+
+## 📊 RecIF-Bench
+
+We propose **RecIF-Bench** to rigorously assess the synergy between instruction following and domain-specific recommendation. It organizes 9 distinct tasks into a four-layer capability hierarchy:
+
+* **Layer 0: Semantic Alignment** (Item Understanding) 
+* **Layer 1: Fundamental Prediction** (Short Video Rec, Ad Rec, Goods Rec, Label Prediction) 
+* **Layer 2: Instruction Following** (Interactive Rec, Label-Conditional Rec) 
+* **Layer 3: Reasoning** (User Summarization, Recommendation Explanation) 
+
+The benchmark aggregates data from three domains: **Short Video** (Content), **Ads** (Commercial), and **Goods** (E-commerce).
+
+## 🤖 Model Zoo
+
+The OpenOneRec-Foundation series is built upon the Qwen architecture, enhanced with **Itemic Tokens** for modality alignment and trained via a multi-stage protocol.
+
+| Model | Backbone | Parameters | Description | Link |
+| :--- | :--- | :--- | :--- | :--- |
+| **OneRec-Open-1.7B** | Qwen3-1.7B | 1.7B | Standard version trained on open-source data (~100B tokens) | [HuggingFace](https://huggingface.co/Onerec) |
+| **OneRec-Open-8B** | Qwen3-8B | 8B | Standard version trained on open-source data (~100B tokens) | [HuggingFace](https://huggingface.co/Onerec) |
+| **OneRec-Pro-1.7B** | Qwen3-1.7B | 1.7B | Enhanced version with proprietary tokens | *Coming Soon* |
+| **OneRec-Pro-8B** | Qwen3-8B | 8B | Enhanced version with proprietary tokens | *Coming Soon* |
+
+## 🏗️ Method & Architecture
+
+OpenOneRec reframes recommendation as a general-purpose sequence modeling paradigm.
+
+### 1. Items as Tokens
+To bridge the modality gap, we treat items as a distinct modality using **Itemic Tokens** derived from hierarchical vector quantization. This allows the LLM to process interaction history as a cohesive context sequence.
+
+### 2. Training Pipeline
+Our framework utilizes the following recipe:
+* **Pre-Training**: Integrates collaborative signals via Itemic-Text Alignment and mixed-domain Co-Pretraining.
+* **Post-Training**:
+    * *Stage 1*: Cold-Start SFT for basic instruction following.
+    * *Stage 2*: Alternating On-Policy Distillation & SFT to balance general reasoning and recommendation performance.
+    * *Stage 3*: Recommendation-oriented Reinforcement Learning (Rec-RL).
+
+<div align="center">
+  <img src="assets/framework.png" width="90%" alt="OpenOneRec Overall Framework" />
+  <br>
+  <em>Figure: The Overall Framework of OpenOneRec.</em>
+</div>
+
+## 📈 Performance
+
+### Results on RecIF-Bench
+OpenOneRec-Foundation achieves **State-of-the-Art (SOTA)** results across RecIF-Bench tasks, significantly outperforming baselines like LC-Rec and TIGER.
+
+| Task | Metric | TIGER | LC-Rec | **OneRec-Pro-8B** |
+| :--- | :--- | :--- | :--- | :--- |
+| **UserDoc** | Recall@32 | 0.0132 | 0.0180 | **0.0369** |
+| **Ads Rec** | Recall@32 | 0.0581 | 0.0723 | **0.0964** |
+| **Goods Rec** | Recall@32 | 0.0283 | 0.0416 | **0.0538** |
+
+
+### Cross-Domain Transferability
+On the **Amazon Benchmark** (10 datasets), OpenOneRec demonstrates exceptional zero-shot/few-shot transfer capabilities, achieving an average **26.8% improvement** in Recall@10 over the second-best method.
+
+## 🚀 Quick Start
+
+*Code release and detailed usage instructions are coming soon.*
+
+Currently, you can load our models using `transformers`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Onerec/OneRec-Open-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+# Example inference code will be updated here
+```
+
+## 📜 Citation
+If you find our work helpful, please cite our technical report:
+
+```bibtex
+@article{openonerec2025,
+  title={An Open Foundation Model and Benchmark to Accelerate Generative Recommendation},
+  author={OneRec Team},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+## 🛡️ License
+The code in this repository is licensed under the Apache 2.0 License. The model weights are subject to their specific license agreements.