Duplicate from Inserloft/PyMini

Co-authored-by: Inserloft <Inserloft@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+language:
+- en
+- es
+task_categories:
+- text-generation
+- fill-mask
+size_categories:
+- 10K<n<100K
+pretty_name: PyMini
+tags:
+- DS Mini
+- python
+- code
+- education
+- small-models
+- fine-tuning
+- Inserloft-Research
+---
+
+<!-- BANNER START -->
+<p align="center">
+  <img src="https://huggingface.co/datasets/Inserloft/PyMini/resolve/main/img/Banner.png" alt="PyMini Banner" width="100%">
+</p>
+<!-- BANNER END -->
+
+
+# PyMini – A Compact Python Instruction Dataset for Small Language Models
+
+**PyMini** is a synthetic instruction dataset built by [Inserloft](https://inserloft.com) to teach Python programming to small language models (<2B parameters). It covers fundamental Python concepts through a diverse set of tasks — code prediction, bug fixing, function completion, explanation, and conceptual Q&A. Available in **English, Spanish, or bilingual** versions.
+
+## Dataset Description
+
+PyMini provides a **high-quality, compact, and fully synthetic** dataset for fine-tuning models that need a solid grasp of Python without unnecessary complexity. Every example is generated programmatically, avoiding license issues and giving exact control over difficulty and coverage.
+
+- **Languages:** English (`en`), Spanish (`es`), or mixed (`both`)
+- **Number of examples:** Configurable; default release contains **~49,200 examples** (balanced across task types)
+- **Format:** Parquet (native), auto-converted from the original generation output
+- **Generated with:** Python script (no web scraping, no copyrighted code)
+
+## Dataset Structure
+
+Each example is a JSON object with three fields:
+
+```json
+{
+  "instruction": "string (the task description)",
+  "input": "string (code snippet or empty)",
+  "output": "string (the expected answer/code/explanation)"
+}
+```
+
+Example (English):
+
+```json
+{
+  "instruction": "What is the output of the following code?",
+  "input": "```python\nprint(3 + 4 * 5)\n```",
+  "output": "23"
+}
+```
+
+The `input` field may be empty for tasks like "Write a function...".
+
+## Task Types and Distribution
+
+| Task Type | Description | Approx. Weight |
+|---|---|---|
+| Predict Output | Given a code snippet, predict what it prints or evaluates to. | 25% |
+| Fix Bug | Correct syntax or logical errors in provided code. | 15% |
+| Fill Missing Line | Complete a partially written function body to fulfill its purpose. | 15% |
+| Write Function | Write a whole function from a natural language specification. | 20% |
+| Explain Code | Explain in plain language what a piece of code does. | 15% |
+| Concept Question | Answer a theoretical question about Python (e.g., "What is a list?"). | 10% |
+| Convert Code | Transform a loop into a comprehension or vice versa. | 5% |
+
+All code examples are self-contained and focus on core Python: variables, types, conditionals, loops, functions, lists, dictionaries, sets, file handling, exceptions, basic OOP, and comprehensions.
+
+## Usage
+
+Load the dataset directly with the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("inserloft/PyMini", split="train")
+print(dataset[0])
+```
+
+The dataset is stored natively in **Parquet** format, so it loads quickly and works out of the box with the `datasets` library, Dataset Viewer, and any Parquet-based tooling (pandas, DuckDB, Polars, etc.):
+
+```python
+import pandas as pd
+
+df = pd.read_parquet("hf://datasets/inserloft/PyMini/train.parquet")
+print(df.head())
+```
+
+## Data Fields
+
+- **instruction** (str): Task description in the chosen language.
+- **input** (str): Optional code block (formatted with ` ```python ` fences) or empty string.
+- **output** (str): Expected response (may contain code blocks, explanations, or short answers).
+
+## Data Splits
+
+The default release includes a single training split. For evaluation, we recommend either:
+
+- Holding out a random 5–10% of the data, or
+- Using a separate, handcrafted test set of real-world Python problems.
+
+## Generation Process
+
+PyMini is entirely synthetic, generated by a Python script that randomly combines templates and safe code evaluation to produce diverse examples. Key aspects of the generation:
+
+- **Deduplication:** A hash-based deduplication step prevents exact duplicate examples.
+- **Reproducibility:** The script uses a fixed random seed (42) for reproducible generation.
+- **Safety:** All code snippets are evaluated in a restricted environment (no file system access, limited built-ins).
+- **Customization:** Adjust the number of examples, language (`--lang es/en/both`), and task weights by modifying the script.
+
+The generation script is included in the repository (`generate_pymini.py`). Output is converted to Parquet for storage and distribution on the Hub.
+
+## Intended Use
+
+This dataset is intended for fine-tuning small language models (under ~2 billion parameters) to improve their Python understanding and code generation abilities. It is particularly suitable for:
+
+- Code assistants that must explain or fix Python code.
+- Educational tools that teach Python basics.
+- Lightweight models running in resource-constrained environments (edge, mobile).
+
+## Out-of-Scope Use
+
+PyMini is not designed for:
+
+- Large-scale production code generation.
+- Mastering advanced Python libraries (NumPy, pandas, Django, etc.).
+- Security-sensitive code auditing.
+
+The examples are deliberately simple and may not cover edge cases of real-world software.
+
+## Bias, Risks, and Limitations
+
+- **Synthetic nature:** The dataset was generated from templates, so it may lack the stylistic variance of human-written code.
+- **Language coverage:** While bilingual, the vocabulary is limited to common Python terminology. Colloquial expressions or complex natural language instructions may be underrepresented.
+- **No malicious code:** The dataset does not include security vulnerabilities or harmful patterns; it is purely educational.
+- **Potential overfitting:** Because of the templated generation, models may memorize patterns rather than generalizing to unseen code. Evaluate on diverse, real-world test sets.
+
+## License
+
+This dataset is released under the MIT License. You are free to use, modify, and distribute it for both research and commercial purposes.
+
+## Citation
+
+If you use PyMini in your work, please cite it as:
+
+```bibtex
+@misc{pymini2025,
+  title        = {PyMini: A Compact Python Instruction Dataset for Small Language Models},
+  author       = {Inserloft},
+  year         = 2025,
+  howpublished = {\url{https://huggingface.co/datasets/inserloft/PyMini}}
+}
+```
+
+## Contributing
+
+Feel free to open issues or pull requests if you have suggestions for new task types, additional languages, or improvements to the generation script.
+
+## Contact
+
+For questions, reach out via the Hugging Face community tab or through [inserloft.com](https://inserloft.com).

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