NextCoder

Azure AI Foundry: 7B, 14B, 32B | 🤗 Hugging Face   |  📑 Paper

Introduction

This repository hosts the official code and data artifact for the paper NextCoder: Robust Learning of Diverse Code Edits .

The work is the development of code-editing LLMs, synthetic data generation pipeline and a novel finetuning methodology called Selective Knowledge Transfer (SeleKT).

Repository Structure

• data: contains the scripts and files required to generate synthetic dataset for code-editing as per the pipeline proposed in the paper
• train contains the scripts and files for finetuning with SeleKT, LoRA and usual SFT.

Model Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "microsoft/NextCoder-7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
Fix the following function that divides two numbers to handle all the edge cases:

def divide(a, b)
  returm a/b
"""
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Evaluation and Performance

Comparison of NextCoder-32B models with other models

Models	HUMANEVALFIX	CANITEDIT	AIDER	POLYGLOT
QwenCoder-2.5-3B	73.2	37.1	36.8	-
QwenCoder-2.5-3B-LoRA	64.6	36.2	35.8	-
QwenCoder-2.5-3B-SFT	76.2	32.4	30.1	-
NextCoder-3B	75.6	42.4	37.6	-
QwenCoder-2.5-7B	73.8	48.1	59.4	-
QwenCoder-2.5-7B-LoRA	70.7	44.3	40.6	-
QwenCoder-2.5-7B-SFT	70.1	36.7	48.9	-
NextCoder-7B	81.1	50.5	65.7	-
QwenCoder-2.5-14B	87.8	58.1	66.9	9.3
QwenCoder-2.5-14B-LoRA	78.0	50.9	66.2	5.3
QwenCoder-2.5-14B-SFT	79.9	42.4	36.8	3.1
NextCoder-14B	89.8	60.2	72.2	12.2
QwenCoder-2.5-32B	90.2	61.0	72.9	16.4
QwenCoder-2.5-32B-LoRA	82.3	52.4	60.2	6.7
QwenCoder-2.5-32B-SFT	81.7	49.5	66.9	8.4
NextCoder-32B	88.9	62.4	74.7	23.6

Comparison of base QwenCoder-2.5 models of different sizes and their SELEKT-enhanced versions across three code editing benchmarks.

Model	MMLU	GSM8K	HumanEval+	MBPP+
Qwen2.5-Coder-7B-Instruct	53.0	83.40	85.4	72.5
NextCoder-7B	54.5	81.65	84.8	72.0
Qwen2.5-Coder-32B-Instruct	71.9	93.71	87.2	76.7
NextCoder-32B	72.7	92.65	85.9	76.4

Generalization properties kept across different benchmarks among base and nextcoder versions

A detailed evaluation and ablations can be found in our paper

Contributing

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Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Citation

@inproceedings{aggarwal2025nextcoder,
author = {Aggarwal, Tushar and Singh, Swayam and Awasthi, Abhijeet and Kanade, Aditya and Natarajan, Nagarajan},
title = {NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits},
booktitle = {International Conference on Machine Learning},
year = {2025},
url = {https://www.microsoft.com/en-us/research/publication/nextcoder-robust-adaptation-of-code-lms-to-diverse-code-edits/},
}