Instructions to use JunlongTong/StreamingLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunlongTong/StreamingLLM with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JunlongTong/StreamingLLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| [Model checkpoints will be released soon.] | |
| ### Model Details | |
| We introduce a new streaming paradigm that enables large language models to achieve strong performance and generalization in streaming settings, without requiring any architectural modifications. | |
| * Streaming-processing: The LLMs process the input as it arrives, incrementally and in real time. | |
| <img src="streaming.gif" alt="Streaming-processing" width="900"/> | |
| * Batch-processing: The LLMs process inputs all at once after receiving the full sequence. | |
| <img src="batch.gif" alt="Batch-processing" width="900"/> | |
| ### Model Sources | |
| - **Paper:** https://arxiv.org/abs/2505.16983 | |
| - **Repository:** https://github.com/EIT-NLP/StreamingLLM | |
| ### Citation | |
| ```tex | |
| @misc{tong2025llmeffectivestreamingprocessor, | |
| title={LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding}, | |
| author={Junlong Tong and Jinlan Fu and Zixuan Lin and Yingqi Fan and Anhao Zhao and Hui Su and Xiaoyu Shen}, | |
| year={2025}, | |
| eprint={2505.16983}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL}, | |
| url={https://arxiv.org/abs/2505.16983}, | |
| } | |
| ``` |