README.md

---
language:
  - code
license: apache-2.0
task_categories:
  - text-generation
tags:
  - code
  - coding
  - synthetic
  - instruction-tuning
  - sharegpt
  - alpaca
  - multi-language
  - 2b-model
  - fine-tuning
size_categories:
  - 1M<n<10M
---

# Synthetic Coding Dataset v1

A large-scale synthetic coding dataset designed for training and fine-tuning ~2B parameter language models. Contains **~1.5M instruction-response pairs** spanning **22 programming languages** and **10 task categories**, formatted in the ShareGPT/Alpaca hybrid conversation schema.

## Dataset Summary

| Property | Value |
|---|---|
| **Total Entries** | ~1,500,000 |
| **Total Size** | ~4.48 GB |
| **Format** | JSONL (63 part files) |
| **Schema** | ShareGPT/Alpaca hybrid (conversations array) |
| **Languages** | 22 programming languages |
| **Task Categories** | 10 types |
| **Difficulty Levels** | 4 (beginner to expert) |
| **Generated** | 2026-06-22 |

## Supported Languages

Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, Ruby, PHP, Kotlin, Swift, Scala, C, Lua, Julia, Elixir, Haskell, OCaml, Dart, R, Bash

## Task Categories

| Category | Description |
|---|---|
| `code_generation` | Write functions, classes, and complete programs |
| `debugging` | Find and fix bugs in existing code |
| `explanation` | Explain programming concepts and code behavior |
| `refactoring` | Improve code quality, readability, and structure |
| `algorithm` | Implement classic and advanced algorithms |
| `system_design` | Design scalable systems and architectures |
| `code_review` | Review and critique code for issues |
| `best_practices` | Language-specific idioms and best practices |
| `design_pattern` | Explain and implement design patterns |
| `data_structure` | Implement and manipulate data structures |

## Difficulty Levels

- **beginner** — Basic syntax, simple loops, conditionals, and introductory concepts
- **intermediate** — Standard design patterns, common algorithms, and typical development tasks
- **advanced** — Complex algorithms, performance optimization, and non-trivial problem solving
- **expert** — System design, architecture decisions, and large-scale engineering challenges

## Data Format

Each line in the JSONL files is a JSON object with the following structure:

```json
{
  "id": "unique_identifier",
  "source": "synthetic_coding_dataset_v1",
  "category": "code_generation",
  "language": "Python",
  "difficulty": "intermediate",
  "conversations": [
    {"from": "human", "value": "Write a function that..."},
    {"from": "gpt", "value": "Here is the implementation..."}
  ],
  "metadata": {
    "task_type": "code_generation",
    "has_code": true,
    "tokens_approx": 1234
  }
}
```

### Field Descriptions

| Field | Type | Description |
|---|---|---|
| `id` | `string` | Unique identifier for the entry |
| `source` | `string` | Dataset source identifier (always `synthetic_coding_dataset_v1`) |
| `category` | `string` | One of the 10 task categories listed above |
| `language` | `string` | Programming language for the task |
| `difficulty` | `string` | One of: `beginner`, `intermediate`, `advanced`, `expert` |
| `conversations` | `array` | Array of message objects with `from` (`human`/`gpt`) and `value` fields |
| `metadata.task_type` | `string` | Mirrors the `category` field |
| `metadata.has_code` | `boolean` | Whether the response contains code blocks |
| `metadata.tokens_approx` | `integer` | Approximate token count for the entry |

## Dataset Structure

The dataset is distributed as 63 JSONL part files:

```
data/
├── coding_dataset_part_001.jsonl
├── coding_dataset_part_002.jsonl
├── ...
└── coding_dataset_part_063.jsonl
```

> **Note:** Two part files (043 and 050) are empty (0 bytes) and can be safely ignored.

## Usage

### Loading with Hugging Face Datasets

```python
from datasets import load_dataset

dataset = load_dataset("your-username/synthetic_coding_dataset_v1", split="train")
print(dataset[0])
```

### Loading Manually

```python
import json

entries = []
for i in range(1, 64):
    filepath = f"data/coding_dataset_part_{i:03d}.jsonl"
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line:
                    entries.append(json.loads(line))
    except FileNotFoundError:
        continue

print(f"Loaded {len(entries)} entries")
```

### Filtering by Language or Category

```python
# Filter Python entries
python_entries = [e for e in dataset if e["language"] == "Python"]

# Filter debugging tasks at advanced level
debugging_advanced = [
    e for e in dataset
    if e["category"] == "debugging" and e["difficulty"] == "advanced"
]
```

## Intended Use

This dataset is designed for:

- **Instruction tuning** of code-generation language models in the ~2B parameter range
- **Fine-tuning** existing base models for coding tasks
- **Research** on multi-language code understanding and generation
- **Benchmarking** code model performance across languages and difficulty levels

## Limitations

- This is a **synthetically generated** dataset. While it covers a broad range of coding tasks, it may not fully represent the complexity and nuance of real-world developer interactions or production codebases.
- The responses are generated by an AI model and may occasionally contain suboptimal solutions, outdated APIs, or minor inaccuracies.
- The dataset has not been manually verified at scale; users are encouraged to perform their own quality filtering based on their specific requirements.

## License

This dataset is released under the **MIT** license.

## Acknowledgements

Generated using large language models for synthetic data creation. Designed to support open-source code model training and research.