---
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>

![ZFusionAI Overview](assert/image.png)

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**).

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:

* **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.* 🎓