Improve model card: Add pipeline tag, library name, paper abstract, and update paper link

README.md

@@ -1,17 +1,23 @@
 ---
-license: apache-2.0
 base_model:
 - TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 - baichuan-inc/Baichuan2-7B-Base
 - Qwen/Qwen3-8B
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # Model Card for HLLM
 
 [![HLLM](https://img.shields.io/badge/HLLM%20paper-2409.12740-da282a.svg)](https://arxiv.org/abs/2409.12740)
-[![HLLM_Creator](https://img.shields.io/badge/HLLM--Creator%20-2508.18118-da282a.svg)](https://arxiv.org/abs/2508.18118)
+[![HLLM_Creator](https://img.shields.io/badge/HLLM--Creator%20-2508.18118-da282a.svg)](https://huggingface.co/papers/2508.18118)
 [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/bytedance/HLLM)
 
+## Paper Abstract
+AI-generated content technologies are widely used in content creation. However, current AIGC systems rely heavily on creators' inspiration, rarely generating truly user-personalized content. In real-world applications such as online advertising, a single product may have multiple selling points, with different users focusing on different features. This underscores the significant value of personalized, user-centric creative generation. Effective personalized content generation faces two main challenges: (1) accurately modeling user interests and integrating them into the content generation process while adhering to factual constraints, and (2) ensuring high efficiency and scalability to handle the massive user base in industrial scenarios. Additionally, the scarcity of personalized creative data in practice complicates model training, making data construction another key hurdle. We propose HLLM-Creator, a hierarchical LLM framework for efficient user interest modeling and personalized content generation. During inference, a combination of user clustering and a user-ad-matching-prediction based pruning strategy is employed to significantly enhance generation efficiency and reduce computational overhead, making the approach suitable for large-scale deployment. Moreover, we design a data construction pipeline based on chain-of-thought reasoning, which generates high-quality, user-specific creative titles and ensures factual consistency despite limited personalized data. This pipeline serves as a critical foundation for the effectiveness of our model. Extensive experiments on personalized title generation for Douyin Search Ads show the effectiveness of HLLM-Creator. Online A/B test shows a 0.476% increase on Adss, paving the way for more effective and efficient personalized generation in industrial scenarios.
+
+## Model Overview
 This repo is used for hosting HLLM and HLLM-Creator checkpoints.
 
 For more details or tutorials see https://github.com/bytedance/HLLM.
@@ -56,4 +62,7 @@ author={Junyi Chen and Lu Chi and Siliang Xu and Shiwei Ran and Bingyue Peng and
 journal={arXiv preprint arXiv:2508.18118},
 year={2025}
 }
-```
+```
+
+> Thanks to the excellent code repository [RecBole](https://github.com/RUCAIBox/RecBole), [VisRec](https://github.com/ialab-puc/VisualRecSys-Tutorial-IUI2021), [PixelRec](https://github.com/westlake-repl/PixelRec) and [HSTU](https://github.com/facebookresearch/generative-recommenders/tree/main) !
+> HLLM is released under the Apache License 2.0, some codes are modified from HSTU and PixelRec, which are released under the Apache License 2.0 and MIT License, respectively.