Instructions to use microsoft/NextCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/NextCoder-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/NextCoder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/NextCoder-7B")
model = AutoModelForCausalLM.from_pretrained("microsoft/NextCoder-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/NextCoder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/NextCoder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/NextCoder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/NextCoder-7B

SGLang

How to use microsoft/NextCoder-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "microsoft/NextCoder-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/NextCoder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "microsoft/NextCoder-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/NextCoder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/NextCoder-7B with Docker Model Runner:
```
docker model run hf.co/microsoft/NextCoder-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NextCoder-7B

GitHub | Paper

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, Paper.

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.

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	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.

Detailed evaluation results are reported in this 📑 paper.

Responsible AI Use

The base models (from the QwenCoder-2.5 family) are suspectible to malicious prompts and may generate or execute harmful code. Our finetuning does not enhance or impede such behaviors. The users should use the models and their outputs responsibly and with caution. Model outputs should be subjected to additional analysis, including manual inspection, and sandboxing before execution.

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/},
}