Datasets:

ghostcc3
/

mix-context-post-training-128k

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:

Downloading public corpora used for short- and long-context content
Tokenizing with a specified tokenizer (default in scripts: meta-llama/Meta-Llama-3-8B)
Filtering and bucketing by token length
Packing sequences to target context windows
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:

@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}
}