Datasets:

InserloftResearch
/

PyMAX

---
license: other
task_categories:
- text-generation
language:
- en
- es
tags:
- python
- code
- synthetic
- inserloft
- inserloft-research
- parquet
- programming
size_categories:
- 100M<n<1B
pretty_name: PyMAX
extra_gated_heading: "PyMAX Terms of Use"
extra_gated_prompt: >
  **IMPORTANT:** By requesting access to PyMAX, you confirm that you have read and agree to the following terms:
  
  1. **Free Use:** You may use this dataset for any purpose, including commercial, academic, or personal projects, free of charge.
  2. **Mandatory Attribution:** If you use PyMAX in your research, to train a model, or in any publication, you MUST explicitly mention **"PyMAX by Inserloft Research (Research.Inserloft.com)"** in the System Card, Model Card, or documentation where you cite public datasets used.
extra_gated_fields:
  Full Name: text
  Organization: text
  Email: text
  I agree to cite PyMAX by Inserloft Research in my Model Card or documentation: checkbox
extra_gated_button_content: "Accept terms and request access"
---
<!-- BANNER START -->
<p align="center">
  <img src="https://huggingface.co/datasets/Inserloft/PyMAX/resolve/main/img/banner.png" alt="PyMAX Banner" width="100%">
</p>
<!-- BANNER END -->

<p align="center">
  <strong>The largest synthetic Python dataset for training and fine-tuning LLMs.</strong>
</p>

<p align="center">
  <img alt="Examples" src="https://img.shields.io/badge/Examples-500%2C000%2C000-FF9D00?style=for-the-badge&logo=python&logoColor=white">
  <img alt="Format" src="https://img.shields.io/badge/Format-Parquet-0B0F19?style=for-the-badge&logo=apachespark&logoColor=white">
  <img alt="License" src="https://img.shields.io/badge/License-Free%20(Attribution%20Required)-FF9D00?style=for-the-badge">
  <img alt="Size" src="https://img.shields.io/badge/Size-16GB-0B0F19?style=for-the-badge">
</p>

---

## 📖 Overview

**PyMAX** is a massive, high-quality synthetic dataset of **500 million Python code examples** created by **Inserloft Research**. It is specifically designed to train and fine-tune Large Language Models (LLMs) specialized in programming, code generation, and software development tasks.

This dataset bridges the gap between general-purpose models and specialized coding models, providing an unprecedented volume of Python-specific training data in a clean, structured, and ready-to-use format. PyMAX is not just a collection of prompts and responses; it is a **foundational-grade dataset** enriched with technical metadata, syntactic validation, and conversational alignment for modern LLMs.

---

## 🔥 Key Features

| Feature | Description |
|---------|-------------|
| **Scale** | 500,000,000 examples (100 Parquet shards) |
| **Languages** | English (50%) & Spanish (50%) |
| **Format** | Parquet (Snappy compression), optimized for streaming |
| **Size** | ~60 GB compressed |
| **Generator** | Enhanced Python script leveraging multiprocessing and AST analysis |
| **Topics** | Basic syntax, OOP, data structures, algorithms, web dev, snippets, etc. |
| **Structure** | Prompt, Response, Code, Explanation, Metadata, Messages, Test Code, Safety Flags |
| **Validation** | Automatic syntactic validation via `ast.parse` |
| **Metadata** | Complexity scores, imports, docstring detection, algorithmic complexity, tags |
| **Quality** | 100% synthetic generation with rigorous filtering and deduplication |
| **Use Cases** | Code LLMs, fine-tuning, instruction tuning, RLHF, evaluation |

---

## 🎯 Use Cases

- **Fine-tuning Code LLMs:** Train models like CodeLlama, StarCoder, or custom architectures.
- **Instruction Tuning:** Build models that understand natural language programming instructions.
- **Benchmarking:** Evaluate coding capability of LLMs.
- **Education:** Create AI tutors for Python learning.
- **Research:** Study code generation, program synthesis, and software engineering.
- **Execution-Based Evaluation:** Leverage included test cases for pass/fail scoring.
- **Safety-Aware Training:** Filter examples using the built-in `is_safe` flag.

---

## 📊 Dataset Structure

Each example in PyMAX contains the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `prompt` | `string` | A question or instruction about Python (English or Spanish) |
| `response` | `string` | Detailed explanation followed by code block |
| `code` | `string` | The raw source code |
| `explanation` | `string` | Textual explanation of the code |
| `messages` | `list` | Conversational format (ChatML): `[{"role": "user", "content": prompt}, {"role": "assistant", "content": response}]` |
| `test_code` | `string` | Validation code (assertions) for execution-based checking |
| `metadata` | `dict` | Contains: `topic`, `subtopic`, `difficulty`, `language`, `python_version` |
| `syntax_valid` | `bool` | Whether the code passes `ast.parse` (always `True` after filtering) |
| `imports` | `list` | List of all imported libraries extracted from the AST |
| `has_docstring` | `bool` | Whether the main module/function/class includes a docstring |
| `complexity_score` | `int` | Sum of lines of code + number of AST nodes |
| `hash` | `string` | SHA-256 hash of `prompt + response` for deduplication |
| `time_complexity` | `string` | Algorithmic time complexity (e.g., "O(N)") |
| `space_complexity` | `string` | Algorithmic space complexity (e.g., "O(1)") |
| `code_length` | `int` | Number of characters in the `code` field |
| `tags` | `list` | Array combining topic, subtopic, and difficulty (e.g., `["data_structures", "lists", "beginner"]`) |
| `is_safe` | `bool` | Whether the code avoids dangerous calls (`eval`, `exec`, `os.system`, `subprocess`) |

---

## 🗂️ Topics Covered

- **Basic Syntax:** Variables, operators, data types.
- **Control Structures:** Conditionals, loops, comprehensions.
- **Functions:** Definitions, decorators, generators, closures.
- **OOP:** Classes, inheritance, polymorphism, magic methods.
- **Data Structures:** Lists, dicts, sets, tuples, custom structures.
- **Algorithms:** Sorting, searching, dynamic programming.
- **File I/O:** Reading/writing files, serialization.
- **Web Development:** Flask, Django, FastAPI basics.
- **Testing:** Unit tests, mocking, pytest.
- **Concurrency:** Threading, asyncio, multiprocessing.
- **Snippets:** Short modular code blocks (utilities, math ops, list manipulations) for focused training.

---

## 🚀 How to Use

### Installation
```python
# Install datasets library
pip install datasets
# Load PyMAX (requires access approval)
from datasets import load_dataset
dataset = load_dataset("InserloftResearch/PyMAX", split="train")
```

### Access a Single Example
```python
example = dataset[0]
print(example["prompt"])
print(example["code"])
print(example["messages"])       # Conversational format
print(example["test_code"])      # Assertions for validation
print(example["time_complexity"])# "O(N)"
```

### Stream Large Batches
```python
from datasets import load_dataset
dataset = load_dataset("InserloftResearch/PyMAX", split="train", streaming=True)
for batch in dataset.iter(batch_size=1000):
    process(batch)
```

---

## 📋 Technical Details

| Metric | Value |
|--------|-------|
| Total Examples | 500,000,000 |
| Languages | English (50%), Spanish (50%) |
| Format | Parquet (Snappy compression) |
| Compressed Size | ~60 GB |
| Number of Shards | 100 (each ~5 million examples) |
| Generation Time | ~1.3 hours (4770 seconds) on 48-core infrastructure |
| Parallelism | `multiprocessing.Pool` with 48 workers |
| Batch Size | 5,000,000 rows per batch (avoids memory overflow) |
| Syntax Validation | Automated via `ast.parse` |
| Deduplication | SHA-256 hash of `prompt+response` |
| Safety Check | Flags dangerous calls (`eval`, `exec`, `os.system`, `subprocess`) |
| Python Version Compatibility | 3.10+ |

---

## ⚙️ Dataset Generation Pipeline

PyMAX was generated using an enhanced Python script by **NaNo 3.4** (Inserloft's proprietary AI model). The pipeline includes:

1. **Extreme Parallel Processing:** Utilizes `multiprocessing.Pool` with 48 workers, fully leveraging the available hardware.
2. **Strategic Batching:** Data is generated and packaged in stable chunks of 5 million rows per batch, resulting in exactly 100 Parquet files. This prevents OOM errors and enables efficient streaming.
3. **AST Analysis:** Each code snippet is parsed using Python's `ast` module to:
   - Validate syntax (`syntax_valid`).
   - Extract imports (`imports`).
   - Detect docstrings (`has_docstring`).
   - Compute complexity score (lines + AST nodes).
4. **Metadata Enrichment:** Algorithmic complexity (`time_complexity`, `space_complexity`), code length, and unified tags are automatically calculated.
5. **Conversational Alignment:** Each example is converted into a ChatML-compatible `messages` list, making the dataset instantly ready for instruction tuning.
6. **Test Case Injection:** Each example includes a `test_code` snippet with assertions, enabling execution-based evaluation and potential RLHF workflows.
7. **Safety Filtering:** A scanner flags examples containing `eval`, `exec`, `os.system`, or `subprocess` calls, marking them with `is_safe=False` for optional exclusion.

---

## ⚖️ License & Terms

This dataset is free to use for any purpose (commercial, academic, or personal) without requiring a paid license. The only condition is mandatory attribution:

**"PyMAX by Inserloft Research (Research.Inserloft.com)"**

You must include this citation in your System Card, Model Card, or documentation where you reference public datasets used.

---

## 🏛️ Citation

If you use PyMAX in your research or publications, please cite:

```bibtex
@misc{pymax2026,
  title={PyMAX: Ultra Dataset for Python Learning},
  author={Inserloft Research},
  year={2026},
  month={August},
  url={https://huggingface.co/datasets/InserloftResearch/PyMAX},
}
```

---

## 👥 About Inserloft Research

Inserloft Research is an AI research organization focused on creating high-quality datasets and models for programming and software development. We specialize in synthetic data generation, code intelligence, and LLM fine-tuning.

### 🌐 Website: [Research.Inserloft.com](https://Research.Inserloft.com)
### 📧 Contact: Inserloft@gmail.com

---

## 📜 Changelog

- **2026-08-24 (v1.0):** Initial release (250M examples, 8GB).
- **2026-08-24 (v2.0):** Major upgrade: 500M examples, AST validation, enriched metadata, ChatML format, test cases, safety flags, and optimized generation pipeline.

---

<p align="center"> <sub>© 2026 Inserloft Research. All rights reserved.</sub> </p>

	---
	license: other
	task_categories:
	- text-generation
	language:
	- en
	- es
	tags:
	- python
	- code
	- synthetic
	- inserloft
	- inserloft-research
	- parquet
	- programming
	size_categories:
	- 100M<n<1B
	pretty_name: PyMAX
	extra_gated_heading: "PyMAX Terms of Use"
	extra_gated_prompt: >
	IMPORTANT: By requesting access to PyMAX, you confirm that you have read and agree to the following terms:

	1. Free Use: You may use this dataset for any purpose, including commercial, academic, or personal projects, free of charge.
	2. Mandatory Attribution: If you use PyMAX in your research, to train a model, or in any publication, you MUST explicitly mention "PyMAX by Inserloft Research (Research.Inserloft.com)" in the System Card, Model Card, or documentation where you cite public datasets used.
	extra_gated_fields:
	Full Name: text
	Organization: text
	Email: text
	I agree to cite PyMAX by Inserloft Research in my Model Card or documentation: checkbox
	extra_gated_button_content: "Accept terms and request access"
	---

	<!-- BANNER START -->
	<p align="center">
	<img src="https://huggingface.co/datasets/Inserloft/PyMAX/resolve/main/img/banner.png" alt="PyMAX Banner" width="100%">
	</p>
	<!-- BANNER END -->

	<p align="center">
	<strong>The largest synthetic Python dataset for training and fine-tuning LLMs.</strong>
	</p>

	<p align="center">
	<img alt="Examples" src="https://img.shields.io/badge/Examples-500%2C000%2C000-FF9D00?style=for-the-badge&logo=python&logoColor=white">
	<img alt="Format" src="https://img.shields.io/badge/Format-Parquet-0B0F19?style=for-the-badge&logo=apachespark&logoColor=white">
	<img alt="License" src="https://img.shields.io/badge/License-Free%20(Attribution%20Required)-FF9D00?style=for-the-badge">
	<img alt="Size" src="https://img.shields.io/badge/Size-16GB-0B0F19?style=for-the-badge">
	</p>

	---

	## 📖 Overview

	PyMAX is a massive, high-quality synthetic dataset of 500 million Python code examples created by Inserloft Research. It is specifically designed to train and fine-tune Large Language Models (LLMs) specialized in programming, code generation, and software development tasks.

	This dataset bridges the gap between general-purpose models and specialized coding models, providing an unprecedented volume of Python-specific training data in a clean, structured, and ready-to-use format. PyMAX is not just a collection of prompts and responses; it is a foundational-grade dataset enriched with technical metadata, syntactic validation, and conversational alignment for modern LLMs.

	---

	## 🔥 Key Features

	\| Feature \| Description \|
	\|---------\|-------------\|
	\| Scale \| 500,000,000 examples (100 Parquet shards) \|
	\| Languages \| English (50%) & Spanish (50%) \|
	\| Format \| Parquet (Snappy compression), optimized for streaming \|
	\| Size \| ~60 GB compressed \|
	\| Generator \| Enhanced Python script leveraging multiprocessing and AST analysis \|
	\| Topics \| Basic syntax, OOP, data structures, algorithms, web dev, snippets, etc. \|
	\| Structure \| Prompt, Response, Code, Explanation, Metadata, Messages, Test Code, Safety Flags \|
	\| Validation \| Automatic syntactic validation via `ast.parse` \|
	\| Metadata \| Complexity scores, imports, docstring detection, algorithmic complexity, tags \|
	\| Quality \| 100% synthetic generation with rigorous filtering and deduplication \|
	\| Use Cases \| Code LLMs, fine-tuning, instruction tuning, RLHF, evaluation \|

	---

	## 🎯 Use Cases

	- Fine-tuning Code LLMs: Train models like CodeLlama, StarCoder, or custom architectures.
	- Instruction Tuning: Build models that understand natural language programming instructions.
	- Benchmarking: Evaluate coding capability of LLMs.
	- Education: Create AI tutors for Python learning.
	- Research: Study code generation, program synthesis, and software engineering.
	- Execution-Based Evaluation: Leverage included test cases for pass/fail scoring.
	- Safety-Aware Training: Filter examples using the built-in `is_safe` flag.

	---

	## 📊 Dataset Structure

	Each example in PyMAX contains the following fields:

	\| Field \| Type \| Description \|
	\|-------\|------\|-------------\|
	\| `prompt` \| `string` \| A question or instruction about Python (English or Spanish) \|
	\| `response` \| `string` \| Detailed explanation followed by code block \|
	\| `code` \| `string` \| The raw source code \|
	\| `explanation` \| `string` \| Textual explanation of the code \|
	\| `messages` \| `list` \| Conversational format (ChatML): `[{"role": "user", "content": prompt}, {"role": "assistant", "content": response}]` \|
	\| `test_code` \| `string` \| Validation code (assertions) for execution-based checking \|
	\| `metadata` \| `dict` \| Contains: `topic`, `subtopic`, `difficulty`, `language`, `python_version` \|
	\| `syntax_valid` \| `bool` \| Whether the code passes `ast.parse` (always `True` after filtering) \|
	\| `imports` \| `list` \| List of all imported libraries extracted from the AST \|
	\| `has_docstring` \| `bool` \| Whether the main module/function/class includes a docstring \|
	\| `complexity_score` \| `int` \| Sum of lines of code + number of AST nodes \|
	\| `hash` \| `string` \| SHA-256 hash of `prompt + response` for deduplication \|
	\| `time_complexity` \| `string` \| Algorithmic time complexity (e.g., "O(N)") \|
	\| `space_complexity` \| `string` \| Algorithmic space complexity (e.g., "O(1)") \|
	\| `code_length` \| `int` \| Number of characters in the `code` field \|
	\| `tags` \| `list` \| Array combining topic, subtopic, and difficulty (e.g., `["data_structures", "lists", "beginner"]`) \|
	\| `is_safe` \| `bool` \| Whether the code avoids dangerous calls (`eval`, `exec`, `os.system`, `subprocess`) \|

	---

	## 🗂️ Topics Covered

	- Basic Syntax: Variables, operators, data types.
	- Control Structures: Conditionals, loops, comprehensions.
	- Functions: Definitions, decorators, generators, closures.
	- OOP: Classes, inheritance, polymorphism, magic methods.
	- Data Structures: Lists, dicts, sets, tuples, custom structures.
	- Algorithms: Sorting, searching, dynamic programming.
	- File I/O: Reading/writing files, serialization.
	- Web Development: Flask, Django, FastAPI basics.
	- Testing: Unit tests, mocking, pytest.
	- Concurrency: Threading, asyncio, multiprocessing.
	- Snippets: Short modular code blocks (utilities, math ops, list manipulations) for focused training.

	---

	## 🚀 How to Use

	### Installation
	```python
	# Install datasets library
	pip install datasets

	# Load PyMAX (requires access approval)
	from datasets import load_dataset

	dataset = load_dataset("InserloftResearch/PyMAX", split="train")
	```

	### Access a Single Example
	```python
	example = dataset[0]
	print(example["prompt"])
	print(example["code"])
	print(example["messages"]) # Conversational format
	print(example["test_code"]) # Assertions for validation
	print(example["time_complexity"])# "O(N)"
	```

	### Stream Large Batches
	```python
	from datasets import load_dataset

	dataset = load_dataset("InserloftResearch/PyMAX", split="train", streaming=True)
	for batch in dataset.iter(batch_size=1000):
	process(batch)
	```

	---

	## 📋 Technical Details

	\| Metric \| Value \|
	\|--------\|-------\|
	\| Total Examples \| 500,000,000 \|
	\| Languages \| English (50%), Spanish (50%) \|
	\| Format \| Parquet (Snappy compression) \|
	\| Compressed Size \| ~60 GB \|
	\| Number of Shards \| 100 (each ~5 million examples) \|
	\| Generation Time \| ~1.3 hours (4770 seconds) on 48-core infrastructure \|
	\| Parallelism \| `multiprocessing.Pool` with 48 workers \|
	\| Batch Size \| 5,000,000 rows per batch (avoids memory overflow) \|
	\| Syntax Validation \| Automated via `ast.parse` \|
	\| Deduplication \| SHA-256 hash of `prompt+response` \|
	\| Safety Check \| Flags dangerous calls (`eval`, `exec`, `os.system`, `subprocess`) \|
	\| Python Version Compatibility \| 3.10+ \|

	---

	## ⚙️ Dataset Generation Pipeline

	PyMAX was generated using an enhanced Python script by NaNo 3.4 (Inserloft's proprietary AI model). The pipeline includes:

	1. Extreme Parallel Processing: Utilizes `multiprocessing.Pool` with 48 workers, fully leveraging the available hardware.
	2. Strategic Batching: Data is generated and packaged in stable chunks of 5 million rows per batch, resulting in exactly 100 Parquet files. This prevents OOM errors and enables efficient streaming.
	3. AST Analysis: Each code snippet is parsed using Python's `ast` module to:
	- Validate syntax (`syntax_valid`).
	- Extract imports (`imports`).
	- Detect docstrings (`has_docstring`).
	- Compute complexity score (lines + AST nodes).
	4. Metadata Enrichment: Algorithmic complexity (`time_complexity`, `space_complexity`), code length, and unified tags are automatically calculated.
	5. Conversational Alignment: Each example is converted into a ChatML-compatible `messages` list, making the dataset instantly ready for instruction tuning.
	6. Test Case Injection: Each example includes a `test_code` snippet with assertions, enabling execution-based evaluation and potential RLHF workflows.
	7. Safety Filtering: A scanner flags examples containing `eval`, `exec`, `os.system`, or `subprocess` calls, marking them with `is_safe=False` for optional exclusion.

	---

	## ⚖️ License & Terms

	This dataset is free to use for any purpose (commercial, academic, or personal) without requiring a paid license. The only condition is mandatory attribution:

	"PyMAX by Inserloft Research (Research.Inserloft.com)"

	You must include this citation in your System Card, Model Card, or documentation where you reference public datasets used.

	---

	## 🏛️ Citation

	If you use PyMAX in your research or publications, please cite:

	```bibtex
	@misc{pymax2026,
	title={PyMAX: Ultra Dataset for Python Learning},
	author={Inserloft Research},
	year={2026},
	month={August},
	url={https://huggingface.co/datasets/InserloftResearch/PyMAX},
	}
	```

	---

	## 👥 About Inserloft Research

	Inserloft Research is an AI research organization focused on creating high-quality datasets and models for programming and software development. We specialize in synthetic data generation, code intelligence, and LLM fine-tuning.

	### 🌐 Website: [Research.Inserloft.com](https://Research.Inserloft.com)
	### 📧 Contact: Inserloft@gmail.com

	---

	## 📜 Changelog

	- 2026-08-24 (v1.0): Initial release (250M examples, 8GB).
	- 2026-08-24 (v2.0): Major upgrade: 500M examples, AST validation, enriched metadata, ChatML format, test cases, safety flags, and optimized generation pipeline.

	---

	<p align="center"> <sub>© 2026 Inserloft Research. All rights reserved.</sub> </p>