Update README.md

README.md

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-license: apache-2.0
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+---
+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.