| --- |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 0 |
| num_examples: 1223305 |
| download_size: 3035467525 |
| dataset_size: 0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: mit |
| language: |
| - en |
| - ta |
| size_categories: |
| - 1M<n<10M |
| --- |
| ## <h1>*🚀 ZFusionAI Pretraining Dataset*</h1> |
|
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|  |
|
|
| Welcome to the **ZFusionAI** repository! This dataset is a carefully curated, high-quality collection of text data designed specifically for training small-to-medium-sized language models (in the **500M to 1B parameter range**). |
|
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| By combining general web knowledge, specialized mathematical reasoning, coding proficiency, and Indic language capabilities, this dataset provides a balanced foundation for versatile model performance. |
|
|
| --- |
|
|
| ## 📊 Dataset Composition |
| We have synthesized four distinct data sources to ensure a balanced distribution of knowledge. The data is processed via weighted interleaving to maintain high structural diversity. |
|
|
| | Data Source | Domain | Weight | Purpose | |
| | :--- | :--- | :--- | :--- | |
| | **FineWeb-Edu** | General Web / Edu | 30% | General world knowledge & language fluency | |
| | **UltraData-Math** | Mathematics | 20% | Logical reasoning & symbolic problem solving | |
| | **Python-Github** | Programming | 20% | Syntax, logic, and functional code generation | |
| | **IndicCorpV2** | Tamil (ta) | 30% | Multilingual support & linguistic diversity | |
|
|
| --- |
|
|
| ## 🛠️ Data Processing Pipeline |
| To ensure the model learns from high-quality tokens, we implemented a rigorous filtering process: |
|
|
| * **Streaming Load:** Efficient memory management by processing data in streams. 🌊 |
| * **Normalization:** Harmonized schema (all columns mapped to `text`). 🔄 |
| * **Length Filtering:** Dropped samples with fewer than 30 words to avoid noise. ✂️ |
| * **Quality Heuristics:** Applied an alphabet/number ratio check (min 30% density) to filter out "junk" characters, HTML boilerplate, and non-informative strings. 🧹 |
|
|
| --- |
|
|
| ## ⚙️ How to use |
| You can load this dataset easily using the Hugging Face `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the ZFusionAI dataset |
| dataset = load_dataset("GTKING/ZFusionAI_Pretraining_Data", streaming=True) |
| |
| # Peek at the first example |
| print(next(iter(dataset))) |
| ``` |
|
|
| --- |
|
|
| ## 💡 Best Practices for 500M-1B Models |
| Given the parameter count, this dataset is optimized to encourage data efficiency: |
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|
| * **Tokenizer:** Ensure you are using a tokenizer compatible with the mixed content (e.g., a byte-level BPE tokenizer) to handle both code and Tamil characters effectively. 🧩 |
| * **Epochs:** With a dataset of this scale, consider 1–3 epochs depending on your compute budget. ⏳ |
| * **Instruction Tuning:** While this is a pre-training corpus, it is highly recommended to follow up with an instruction-tuning phase using specialized datasets for optimal chat performance. 🤖 |
|
|
| ## ⚖️ License & Credits |
| * **FineWeb-Edu:** [HuggingFaceFW](https://huggingface.co/HuggingFaceFW) |
| * **UltraData-Math:** [OpenBMB](https://huggingface.co/openbmb) |
| * **IndicCorpV2:** [AI4Bharat](https://huggingface.co/ai4bharat) |
| * **Python-Github:** Sourced via [Hugging Face](https://huggingface.co) |
|
|
| *This dataset is provided for research and development purposes.* 🎓 |
