Magicoder-OSS-Instruct-75K — LLM Code Dataset
Uses real open-source code snippets as seeds to generate diverse coding problems and solutions with GPT-4. The OSS-Instruct method produces more realistic, grounded coding tasks than purely synthetic approaches. Magicoder-S-DS-6.7B trained on this data surpassed GPT-3.5-Turbo on HumanEval.
Dataset Details
| Provider | ise-uiuc |
| Category | Code |
| Size | 75k Rows |
| License | MIT |
| Downloads | 250k |
| Tags | Code-Generation, GPT-4, OSS-Grounded, Python |
from datasets import load_dataset
ds = load_dataset("ise-uiuc/Magicoder-OSS-Instruct-75K")
Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
| 7B QLoRA | ~6GB VRAM |
| 13B QLoRA | ~10GB VRAM |
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Frequently asked questions
Can I use Magicoder-OSS-Instruct-75K commercially?
Yes — Magicoder-OSS-Instruct-75K is released under MIT, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does Magicoder-OSS-Instruct-75K contain, and do I need all of it?
Magicoder-OSS-Instruct-75K contains 75k Rows. You rarely need all of it: for style and format fine-tuning, a few hundred to a few thousand examples are enough — load a slice (e.g. split="train[:1000]") and scale up only if quality plateaus.
What is Magicoder-OSS-Instruct-75K best used for?
Code instruction tuning grounded in real open-source snippets. It belongs to the Code section of our dataset hub, where you'll find alternatives and complementary sets.
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