| license: mit | |
| task_categories: | |
| - text-generation | |
| tags: | |
| - long-sequence-generation | |
| - training-data | |
| - llm-training | |
| dataset_info: | |
| features: | |
| - name: input_ids | |
| sequence: int32 | |
| splits: | |
| - name: train | |
| num_bytes: 1126604624 | |
| num_examples: 27553 | |
| download_size: 521687503 | |
| dataset_size: 1126604624 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| This dataset provides training data for accelerating ultra-long sequence generation with large language models (LLMs). It's used in the paper [From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation up to 100K Tokens](https://hf.co/papers/2502.18890). | |
| The data is derived from the PG-19 dataset, filtering out sequences smaller than 8K tokens to focus on ultra-long sequences relevant for the TokenSwift method described in the paper. | |
| Code: https://github.com/bigai-nlco/TokenSwift |