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.
Dataset Details
Provider
m-a-p
Category
Code
Size
74k Rows
License
Apache 2.0
Downloads
180k
Tags
Multi-Language, Python, Java, Code-Chat
from datasets import load_dataset
ds = load_dataset("m-a-p/CodeFeedback-Filtered-Instruction")
Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
OpenCodeReasoning — Fine-tuning coding models that show their reasoning before writing the solution
The Stack v2 — Pretraining code models across 600+ programming languages
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.