---
license: mit
language:
- en
tags:
- rust
- code
- instruction-tuning
- magicoder
- oss-instruct
pretty_name: Magicoder OSS Instruct Rust (3.9K Cleaned)
size_categories:
- 1K<n<10K
task_categories:
- text-generation
---

# 🦀 Magicoder-OSS-Instruct-Rust (3.9K Cleaned)

**Magicoder-OSS-Instruct-Rust** is a high-quality, syntax-verified dataset of **3,909** Rust coding instructions derived from real-world open-source GitHub projects.

This dataset is extracted from [ise-uiuc/Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K), filtered specifically for Rust, and validated via in-memory compiler checks. No language translation was applied; the dataset remains in its original English format.

---

## ⚙️ Filtering and Verification Methodology

The dataset was processed using the following technical pipeline:

* **Language Filtering:** Extracted entries strictly where `lang: rust`, removing all other programming languages.
* **In-Memory Syntax Validation (`rustc` RAM Check):** All code snippets were evaluated on-the-fly via `rustc --crate-type=lib` through standard input (`stdin`). Entries with broken syntax, unclosed braces, or invalid AST structures were automatically discarded.
* **Format Standardization:** Converted raw problem/solution pairs into standard ChatML (`messages`) format for direct compatibility with SFT and Fine-Tuning frameworks (LoRA / QLoRA).

---

## ⚠️ Filtering Summary (75K -> 3.9K)

Out of the original 75,000 multi-language samples:
1. Non-Rust programming languages were filtered out.
2. Incomplete snippets or entries failing `rustc` syntax checks were removed.
3. A clean, verified set of **3,909 high-quality Rust instructions** was produced.

---

## 📊 Dataset Structure

The dataset follows the **ChatML** (`messages`) format:

* **`system`**: System prompt assigning the expert Rust developer role.
* **`user`**: Rust programming task, module design requirement, or algorithmic problem.
* **`assistant`**: Corresponding idiomatic Rust solution.

### Schema Details

| Field | Type | Description |
|---|---|---|
| `id` | `int64` | Original dataset index |
| `task_category` | `string` | Task category identifier (`oss_instruct`) |
| `messages` | `list` | Conversation history containing `role` and `content` (`system`, `user`, `assistant`) |

---

## 💡 Example Entry

```json
{
  "id": 1601,
  "task_category": "oss_instruct",
  "messages": [
    {
      "role": "system",
      "content": "You are an expert assistant who solves Rust programming problems and generates correct code."
    },
    {
      "role": "user",
      "content": "You are tasked with creating a Rust module system for a 2D game engine..."
    },
    {
      "role": "assistant",
      "content": "```rust\n// input_handler.rs\npub mod input_handler {\n    pub struct InputHandler {\n        // Implementation details\n    }\n}\n\n// input.rs\npub mod input {\n    pub use input_handler::InputHandler;\n    pub use keyboard::Keyboard;\n    pub use mouse::Mouse;\n    pub use gamepad::Gamepad;\n}\n```\n\nIn this solution, we have created the input modules..."
    }
  ]
}
```

---

## 💻 Usage

```python
from datasets import load_dataset

dataset = load_dataset("WrittenWithRust/Magicoder-OSS-Instruct-Rust-3.9K")
print(dataset["train"][0])
```

---

## 📜 Citation

```bibtex
@article{wei2023magicoder,
  title={Magicoder: Source Code Is All You Need},
  author={Wei, Yuxiang and Wang, Zhe and Liu, Jiawei and Ding, Yuhang and Zhang, Lingming},
  journal={arXiv preprint arXiv:2312.02120},
  year={2023}
}
```