Datasets:

ArcOffical
/

PiCo-DATA-code

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:

{
  "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

from datasets import load_dataset

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

Loading Manually

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

# 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.