---
license: mit
task_categories:
- text-generation
language:
- ps
tags:
- chain-of-thought
- reasoning
- orca
- pashto-ai
- instruction-tuning
size_categories:
- 10K<n<100K
---
This is a Pashto translation of CoT instructions of [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset.

# Pashto_OrcaCoT: A High-Quality Chain-of-Thought Dataset for Pashto Reasoning

**Pashto_OrcaCoT** is the premier open-source, high-quality Chain-of-Thought (CoT) dataset natively optimized for the Pashto language. Built upon rigorous multi-step logic and structured reasoning patterns inspired by the Orca framework, this dataset bridges the gap between international LLM benchmarks and Pashto-native artificial intelligence alignment.

It serves as the foundational reasoning layer for the `iPashto.ai` engine and acts as a critical alignment asset for advanced models, including the upcoming *Qwen3-1.7B-Pashto-Gold* pipeline.

---

## 🌟 Key Features

- **Structured CoT:** Features multi-step logic explicit flows, breakdown reasoning, and rigorous, verifiable formatting.
- **Pashto-Native Alignment:** System prompts and instructions are perfectly contextualized to respect Pashto linguistic nuances, cultural subtexts, and stylistic accuracy.
- **High-Quality Integrity:** Optimized to counteract hallucinations, maximize logical consistency, and ensure deep reasoning depth across mathematics, logic, and common-sense QA.
- **Zero-Scraping Strategy:** Curated with precision, bypassing noisy web-scraping to deliver gold-standard, production-ready instruction tuning.

---

## 📊 Dataset Schema

Each record follows a strict three-field sequence designed for seamless Integration during model training:

| Field | Type | Description |
| :--- | :--- | :--- |
| `instruction` | `string` | The system prompt or agent persona defining the behavioral boundaries. |
| `input` | `string` | The user query, task statement, or reasoning puzzle in Pashto. |
| `output` | `string` | The gold-standard response containing the step-by-step reasoning logic and final answer. |

### Data Example

```json
{
  "instruction": "تاسو د مصنوعي ځیرکتیا (AI) یو مرستیال یاست چې له خلکو سره د معلوماتو په موندلو کې مرسته کوي. کاروونکی له تاسو څخه پوښتنه کوي. ستاسو دنده دا ده چې تر خپله وسه په پوره صداقت ځواب ورکړئ. د ځواب ورکولو پر مهال ګام په ګام فکر وکړئ او د خپل ځواب لپاره منطقي دلایل وړاندې کړئ.",
  "input": "جیمز د 5 اصلي کرکټرونو او 4 ملاتړي کرکټرونو سره د تلویزیون خپرونه پرمخ وړي. هغه په ​​هر قسط کې د ملاتړي کرکټرونو $ 15,000 تادیه کوي. هغه اصلي کرکټرونه درې چنده ډیر ورکړي. هغه په ​​هره برخه کې څومره پیسې ورکوي؟ راځئ چې د امکان تر حده مشخص وي.",
  "output": "جیمز د ملاتړ کونکي کرکټرونو په هره برخه کې $15,000 تادیه کوي. له هغه ځایه چې 4 ملاتړي کرکټرونه شتون لري، دا دوی ته په ټوله برخه کې 4 *$ 15,000 = $60,000 ورکوي.\n\nاصلي کرکټرونه درې ځله ډیر تادیه کیږي. نو، هر اصلي کرکټر په هره برخه کې$ 3 * $15,000 =$ 45,000 ترلاسه کوي.\n\nدلته 5 اصلي کرکټرونه دي، نو هغه دوی ته په هر قسط کې 5 * $45,000 =$ 225,000 تادیه کوي.\n\nپه مجموع کې، جیمز ته $225,000 (اصلي کرکټرونه) +$ 60,000 (ملاتړي کرکټرونه) = $ 285,000 په هره قسط ورکول کیږي."
}

```

---

## 🛠️ Motivation & Vision

Historically, low-resource languages like Pashto have suffered from noisy, poorly translated datasets that degrade an LLM's capability to reason coherently. **Pashto_OrcaCoT** marks the end of the zero-scraping era for Pashto AI, providing a clean, hand-crafted, and machine-optimized environment for supervised fine-tuning (SFT). The goal is to allow Pashto language models to perform complex chain-of-thought deductions at par with English and other globally rich resource environments.

---

## 📜 Citation & Attributions

If you use this dataset in your research, model fine-tuning pipelines, or evaluation benchmarks, please cite it using the following format:

```bibtex
@dataset{nassimjp_pashto_orcacot_2026,
  author       = {Nassim},
  title        = {Pashto_OrcaCoT: A High-Quality Chain-of-Thought Dataset for Pashto Reasoning},
  year         = {2026},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Datasets},
  howpublished = {\url{[https://huggingface.co/datasets/nassimjp/Pashto_OrcaCoT](https://huggingface.co/datasets/nassimjp/Pashto_OrcaCoT)}}
}

```

---

### 🤝 Feedback and Collaboration

For issues, contributions, or collaborations regarding Pashto reasoning tools and native LLM development, please open an issue in the repository or contact us via **iPashto.ai**.