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 ](https://www.microsoft.com/en-us/research/publication/nextcoder-robust-adaptation-of-code-lms-to-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](data/): contains the scripts and files required to generate synthetic dataset for code-editing as per the pipeline proposed in the paper - [train](src/train/) contains the scripts and files for finetuning with SeleKT, LoRA and usual SFT. ## Model Usage ```python 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**
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## Citation
```bibtex
@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/},
}
```