Mungert/NextCoder-7B-GGUF

NextCoder-7B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 5dd942de.

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Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

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Click here to get info on choosing the right GGUF model format

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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/},
}

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊