datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NextCoderDataset
NextCoderDataset
GitHub | Paper
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits (ICML'2025)
Data Overview
NextCoderdataset is the instruction-variant of synthetic dataset, used for training models on code-editing scenarios and compromised of around 381k (127k*3) samples across 8 different programming languages: Python,
Java, C++, C, Rust, Javascript, Go and Kotlin.
This is used to finetune the NextCoder family models using the novel… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/NextCoderDataset.nextcoder-deepseek-distilledNextCoderDataset-Conversational
NextCoderDataset-Conversational
GitHub | Paper
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits (ICML'2025)
Data Overview
NextCoderDataset-Conversational is the multi-turn conversational-variant of synthetic dataset, used for training models on code-editing scenarios and compromised of around 57k samples across 8 different programming languages: Python,
Java, C++, C, Rust, Javascript, Go and Kotlin.
This is used to finetune the NextCoder… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/NextCoderDataset-Conversational.reasoning-sft-NextCoderDataset-100K
NextCoderDataset (converted)
Converted version of microsoft/NextCoderDataset, subsampled to 100,000 rows equally distributed across 8 programming languages for reasoning SFT training.
Format
Each row has three columns:
input - list of dicts with system and user messages (system prompt sets expert code editor role, user prompt contains the editing instruction and original code)
response - response string with <think> reasoning block followed by the edited code in markdown… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/reasoning-sft-NextCoderDataset-100K.NextCoderDataset-parsedA version of microsoft/NextCoderDataset parsed and processed.
You can check the data processing here
The filenames have been generated using a AWQ quantized version of the Qwen3-Coder-30B-A3B-Instruct model:
python scripts/get_file_names.py --base-url "http://localhost:8000" --model "cpatonn/Qwen3-Coder-30B-A3B-Instruct-AWQ" --output "data/filenames.jsonl"
Cleaning the data
We make some assumptions and generalizations:
To get the filename of each example, the first code block… See the full description on the dataset page: https://huggingface.co/datasets/Vokturz/NextCoderDataset-parsed.NextCoderDataset-datasetNextCoderDataset_fix-dataset