---
license: apache-2.0
task_categories:
- text-generation
language:
- en
- zh
pretty_name: MiniModel Pretraining Corpus
---

# Dataset Card for MiniModel Pretraining Corpus

This dataset is a curated, tokenized pretraining mixture designed specifically for training **MiniModel**-series small language models. It was tokenized using the **Mistral-7B-Instruct-v0.3 tokenizer** (vocab size: 32,768), which is included in the [MiniModel-200M-Base repository](https://huggingface.co/xTimeCrystal/MiniModel-200M-Base).

For **training code**, **data loading utilities**, and full reproducibility (including the training script), see the official GitHub repository:  
🔗 [https://github.com/xTimeCrystal/MiniModel/tree/main](https://github.com/xTimeCrystal/MiniModel/tree/main)

## Dataset Details

### Dataset Description

- **Curated by:** xTimeCrystal  
- **Languages:** English, Chinese, Python (code)  
- **License:** Apache 2.0  
- **Intended use:** Pretraining efficient small language models (e.g., MiniModel-200M-Base)  
- **Token count:** ~10 billion tokens  

This corpus combines high-quality educational and general-purpose text sources, filtered and balanced to maximize learning efficiency in low-compute training regimes.

### Source Data Composition

The dataset is a weighted mixture of the following sources (by token count):

- **70%** [`openbmb/Ultra-FineWeb`](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) (English subset)  
- **20%** [`openbmb/Ultra-FineWeb`](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) (Chinese subset)  
- **5%** [`Avelina/python-edu-cleaned`](https://huggingface.co/datasets/Avelina/python-edu-cleaned)  
- **5%** [`HuggingFaceTB/finemath`](https://huggingface.co/datasets/HuggingFaceTB/finemath)  

All source datasets are publicly available and compatible with the Apache 2.0 license.

### Preprocessing

- Tokenized with the **Mistral-7B-Instruct-v0.3 tokenizer**  
- Sequences were packed using a bin-packing algorithm to minimize padding (final padding < 5%)  
- Maximum sequence length: 2048 tokens  
- No deduplication beyond source-level filtering  

> 💡 **Note**: The tokenizer, training configuration, and data-loading pipeline are provided in the [GitHub repo](https://github.com/xTimeCrystal/MiniModel/tree/main) for full reproducibility.