Datasets:
File size: 4,421 Bytes
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language:
- en
license: mit
task_categories:
- text-generation
tags:
- long-context
- post-training
- context-window-extension
- packed-sequences
- continual-training
pretty_name: Mix-Context Post-Training 128K
dataset_info:
features:
- name: input_ids
sequence: int32
- name: position_ids
sequence: int64
splits:
- name: train
num_bytes: 113246784000
num_examples: 72000
download_size: 34848646144
dataset_size: 113246784000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Mix-Context Post-Training Dataset for 128K Context Extension
## Overview
**Mix-Context Post-Training 128K** is a dataset designed specifically for **post-training context window extension** of pretrained LLMs.
It targets the stage *after base pretraining*, where a model is adapted to operate over **much longer contexts (up to 128K tokens)** while preserving short-context behavior. The dataset mixes short- and long-context packed sequences with a controlled length distribution to support:
- Post-training context window extension
- Length generalization / robustness evaluation
- Continued training after positional / RoPE scaling methods
If you use this dataset for post-training, context window extension, or evaluation, **please cite this dataset** (see Citation).
## Sequence Length Distribution and Data Sources
| Context Type | Token Length Range | Packed Context Length | Samples | Data Source |
|-------------|-------------------|----------------------|---------|-------------|
| Short | 64 – 2,048 | 8K | 8,000 | FineWeb-Edu (sample/10BT) |
| Short | 2,048 – 4,096 | 8K | 8,000 | FineWeb-Edu (sample/10BT) |
| Short | 4,096 – 9,216 | 8K | 16,000 | FineWeb-Edu (sample/10BT) |
| Long | 8K – 32K | 128K | 8,000 | RedPajama-Data-1T (arXiv, Wikipedia, Common Crawl) |
| Long | 32K – 64K | 128K | 8,000 | RedPajama-Data-1T (arXiv, Wikipedia, Common Crawl) |
| Long | 64K – 128K | 128K | 16,000 | RedPajama-Data-1T (arXiv, Common Crawl) |
| Long | 128K – 200K | 128K | 8,000 | RedPajama-Data-1T (arXiv, Common Crawl) |
| **Total** | — | — | **72,000** | — |
---
## Dataset Format
Each example is a **packed sequence** ready for causal LM training:
- `input_ids`: token IDs
- `position_ids`: positional indices aligned to the packed sequence
**Note:** This dataset does **not** include raw text. It contains tokenized, packed sequences produced by the preprocessing pipeline.
---
## Construction Summary (High-Level)
This dataset is generated by:
1. Downloading public corpora used for short- and long-context content
2. Tokenizing with a specified tokenizer (default in scripts: `meta-llama/Meta-Llama-3-8B`)
3. Filtering and bucketing by token length
4. Packing sequences to target context windows
5. Concatenating short- and long-context components into the final dataset
### Tokenizer
- Tokenizer name/path: `meta-llama/Meta-Llama-3-8B`
- Each text is encoded with explicit BOS/EOS:
- `BOS + text + EOS`
- Length statistics and buckets are **tokenizer-dependent**
### Short-Context Component
- Source: FineWeb-Edu (`HuggingFaceFW/fineweb-edu`, `sample/10BT`)
- Bucketed by token length (target sample sizes):
- 64–2,048: 8,000
- 2,048–4,096: 8,000
- 4,096–9,216: 16,000
- Packed to **8K context** (short context length)
### Long-Context Component
- Source: RedPajama-Data-1T (`togethercomputer/RedPajama-Data-1T`)
- Splits used:
- `arxiv`
- `wikipedia`
- `common_crawl` (subset used in preprocessing)
- Documents are filtered before tokenization by raw byte length (approx):
- min: 32 KB
- max: 800 KB
- After tokenization, long sequences are filtered and bucketed in token ranges:
- 8K–32K, 32K–64K, 64K–128K, 128K–200K
- Packed to **128K context** (long context length)
### Packing / Sequence Construction
Packing concatenates tokenized samples sequentially until reaching `max_seq_len`:
- `max_seq_len = 128K`
- Short packing `context_len = 8K`
- Long packing `context_len = 128K`
---
## Citation
If you use this dataset, please cite:
```bibtex
@dataset{wang_chen_mix_context_post_training_128k_2026,
author = {Qi Wang and Lizhang Chen},
title = {Mix-Context Post-Training Dataset for 128K Context Extension},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/ghostcc3/mix-context-post-training-128k}
}
|