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@@ -35,3 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 assets/framework.png filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/benchmark.png filter=lfs diff=lfs merge=lfs -text
+assets/onerec.png filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

@@ -1,3 +1,9 @@
+---
+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">
@@ -26,13 +32,13 @@
 
 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, Product).
-* **OneRec-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.
+* **OneRec-Foundation Models**: A family of models (1.7B & 8B) built on the Qwen3 backbone. The series includes **Standard** versions trained on our open-source dataset and **Pro** versions enhanced with a hundred-billion-token industrial corpus from Kuaishou.
 * **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
 
-* **[2026.1.1]** 🎉 **OneRec-Foundation** models (1.7B, 8B) are now available on Hugging Face!
 * **[2026.1.1]** 📑 The technical report has been released.
+* **[2026.1.1]** 🎉 **OneRec-Foundation** models (1.7B, 8B) are now available on Hugging Face!
 * **[2026.1.1]** 🚀 **RecIF-Bench** dataset and evaluation scripts are open-sourced.
 
 ## 📊 RecIF-Bench
@@ -52,10 +58,10 @@ The OpenOneRec-Foundation series is built upon the Qwen architecture, enhanced w
 
 | Model | Backbone | Parameters | Description | Link |
 | :--- | :--- | :--- | :--- | :--- |
-| **OneRec-Open-1.7B** | Qwen3-1.7B | 1.7B | Standard version trained on open-source data (~33B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-1.7B) |
-| **OneRec-Open-8B** | Qwen3-8B | 8B | Standard version trained on open-source data (~33B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-8B) |
-| **OneRec-Pro-1.7B** | Qwen3-1.7B | 1.7B | Scaled-up version with expanded datasets (~130B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-1.7B-pro) |
-| **OneRec-Pro-8B** | Qwen3-8B | 8B | Scaled-up version with expanded datasets (~130B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-8B-pro) |
+| **OneRec-1.7B** | Qwen3-1.7B | 1.7B | Standard version trained on open-source data (~33B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-1.7B) |
+| **OneRec-8B** | Qwen3-8B | 8B | Standard version trained on open-source data (~33B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-8B) |
+| **OneRec-1.7B-Pro** | Qwen3-1.7B | 1.7B | Scaled-up version with expanded datasets (~130B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-1.7B-pro) |
+| **OneRec-8B-Pro** | Qwen3-8B | 8B | Scaled-up version with expanded datasets (~130B tokens) | [HuggingFace](https://huggingface.co/OpenOneRec/OneRec-8B-pro) |
 
 ## 🏗️ Method & Architecture
 
@@ -94,6 +100,12 @@ OpenOneRec-Foundation achieves **State-of-the-Art (SOTA)** results across RecIF-
 | **Item Und.** | LLM Score | -- | -- | 0.2517 | 0.3175 | 0.3202 | 0.3133 | **0.3209** |
 | **Rec. Explanation** | LLM Score | -- | -- | 3.9350 | 3.3540 | 3.6774 | 3.5060 | **4.0381** |
 
+<div align="center">
+  <img src="assets/benchmark.png" width="80%" alt="Holistic Performance Overview of OpenOneRec." />
+  <br>
+  <em>Holistic Performance Overview of OpenOneRec.</em>
+</div>
+
 ### 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.
 
@@ -116,14 +128,54 @@ On the **Amazon Benchmark** (10 datasets), OpenOneRec demonstrates exceptional z
 
 *Code release and detailed usage instructions are coming soon.*
 
-Currently, you can load our models using `transformers`:
+Currently, you can load our models using `transformers>=4.51.0`:
 
 ```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
+
+model_name = "OpenOneRec/OneRec-8B"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+# case - prompt with itemic tokens
+prompt = "这是一个视频：<|sid_begin|><s_a_340><s_b_6566><s_c_5603><|sid_end|>，帮我总结一下这个视频讲述了什么内容"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+# parsing thinking content
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+print("thinking content:", thinking_content)
+print("content:", content)
 ```
 
 ## 📜 Citation
@@ -139,3 +191,13 @@ If you find our work helpful, please cite our technical report:
 ```
 ## 🛡️ License
 The code in this repository is licensed under the Apache 2.0 License. The model weights are subject to their specific license agreements.
+
+## 🙏 Acknowledgements
+
+OpenOneRec is built upon and inspired by the open-source ecosystem. We would like to thank:
+
+- **Qwen3**: for providing the base architecture and model initialization that OpenOneRec builds upon.
+- **General-domain data sources**: for the public corpora referenced in `data/general_text`(github链接) used for mixed-domain training.
+- **VeRL & PyTorch distributed training**: for the training infrastructure and scalable primitives (e.g., **FSDP**) used in post-training and large-scale runs.
+
+We sincerely thank these projects for their outstanding work.