CodeFeedback-Filtered-Instruction
74k coding instruction pairs across Python, Java, JavaScript, and C++ with a focus on interactive code generation and debugging. Combines data from Magicoder, ShareGPT-Python, and original generation to create a diverse multi-language coding chat dataset. Used to train OpenCodeInterpreter.
from datasets import load_dataset
ds = load_dataset("m-a-p/CodeFeedback-Filtered-Instruction")Preview a sample row
{
"query": "Write a JavaScript function to deep-clone an object without using JSON.parse/stringify.",
"answer": "function deepClone(obj) {\n if (obj === null || typeof obj !== 'object') return obj;\n if (Array.isArray(obj)) return obj.map(deepClone);\n return Object.fromEntries(Object.entries(obj).map(([k, v]) => [k, deepClone(v)]));\n}"
}Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
Frequently asked questions
Can I use CodeFeedback-Filtered-Instruction commercially?
Yes — CodeFeedback-Filtered-Instruction is released under Apache 2.0, 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 CodeFeedback-Filtered-Instruction contain, and do I need all of it?
CodeFeedback-Filtered-Instruction contains 74k 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 CodeFeedback-Filtered-Instruction best used for?
Training coding models that respond to execution feedback. It belongs to the Code section of our dataset hub, where you'll find alternatives and complementary sets.