---
license: mit
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- chat
- microsoft
- nextcoder
- selekt
datasets:
- microsoft/NextCoderDataset
- microsoft/NextCoderDataset-Conversational
---


# NextCoder-7B
<p align="center">
        <a href="https://github.com/microsoft/NextCoder">GitHub</a>&nbsp&nbsp | &nbsp&nbsp <a href="https://arxiv.org/abs/2503.03656">Arxiv</a> 
</p>

> NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits (ICML'2025)

## Introduction

NextCoder is the latest series of Code-Editing large language models developed using the Qwen2.5-Coder Instruct variants as base and trained with novel Selective Knowledge Transfer finetuning methodology as introduced in the paper. NextCoder family model comes in 3 different sizes 7, 14, 32 billion parameters, to meet the needs of different developers.
Following are the key improvements:
- Significantly improvements in **code editing**, NextCoder-32B has performing on par with GPT-4o on complex benchmarks like Aider-Polyglot with performance increment of 44% from their base model.
- No loss of generalizibility, due to our new finetuning method **SeleKT**
- **Long-context Support** up to 32K tokens.

**This repo contains the NextCoder-7B model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Post-training with SeleKT
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 7.61B
- Number of Paramaters (Non-Embedding): 6.53B
- Number of Layers: 28
- Number of Attention Heads (GQA): 28 for Q and 4 for KV
  
For more details, please refer to our [blog](), [GitHub](https://github.com/microsoft/NextCoder), [Arxiv](https://arxiv.org/abs/2503.03656).

## Requirements

The code of NextCoder is based on Qwen2.5 base models which has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.

With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```

## Quickstart

Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.

```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

| Models | HUMANEVALEDIT | 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-2.5-14B | 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.*

**Detailed evaluation results are reported in this [📑 paper](https://arxiv.org/abs/2503.03656).**

## Citation

```bibtex
@misc{aggarwal2025robustlearningdiversecode,
      title={Robust Learning of Diverse Code Edits}, 
      author={Tushar Aggarwal and Swayam Singh and Abhijeet Awasthi and Aditya Kanade and Nagarajan Natarajan},
      year={2025},
      eprint={2503.03656},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2503.03656}, 
}
```