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?
🎉 In the Berkeley Function Calling Leaderboard, Nanbeige4-3B-Thinking-2511 secures #25 overall—ranking among the top 10 open-source models and outperforming Qwen3-32B. This highlights its strong agentic reasoning and reliable function-calling capability, despite its compact size.
🎉 Nanbeige4-3B-Thinking-2511 ranks #15 on EQBench3, demonstrating human-preference alignment and emotional intelligence comparable to much larger models.
🎉 Nanbeige4-3B-Thinking-2511 debuts at #11 on WritingBench! Despite only 3B parameters, its creative-writing ability chops rival those of hundred-billion-parameter giants.
Introduction
Nanbeige4-3B-Thinking-2511 is an enhanced iteration over our previous Nanbeige4-3B-Thinking-2510.
Through advanced knowledge distillation techniques and targeted reinforcement learning (RL) optimization, we have significantly scaled the model’s reasoning capabilities, delivering stronger and more reliable performance on diverse challenging benchmarks.
This version establishes new state-of-the-art (SOTA) results among open models under 32B parameters on AIME, GPQA-Diamond, Arena-Hard-V2, and BFCL-V4, which marks a major milestone in delivering powerful yet efficient reasoning capabilities at a compact scale.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
'Nanbeige/Nanbeige4-3B-Thinking-2511',
use_fast=False,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
'Nanbeige/Nanbeige4-3B-Thinking-2511',
torch_dtype='auto',
device_map='auto',
trust_remote_code=True
)
messages = [
{'role': 'user', 'content': 'Help me check the weather in Beijing now'}
]
tools = [{'type': 'function',
'function': {'name': 'SearchWeather',
'description': 'Find out current weather in a certain place on a certain day.',
'parameters': {'type': 'dict',
'properties': {'location': {'type': 'string',
'description': 'A city in china.'},
'required': ['location']}}}}]
prompt = tokenizer.apply_chat_template(
messages,
tools,
add_generation_prompt=True,
tokenize=False
)
input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_ids
output_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)
resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)
print(resp)
Limitations
While we place great emphasis on the safety of the model during the training process, striving to ensure that its outputs align with ethical and legal requirements, it may not completely avoid generating unexpected outputs due to the model's size and probabilistic nature. These outputs may include harmful content such as bias or discrimination. Please don't propagate such content. We do not assume any responsibility for the consequences resulting from the dissemination of inappropriate information.
Citation
If you find our model useful or want to use it in your projects, please cite as follows:
@misc{yang2025nanbeige43btechnicalreportexploring,
title={Nanbeige4-3B Technical Report: Exploring the Frontier of Small Language Models},
author={Chen Yang and Guangyue Peng and Jiaying Zhu and Ran Le and Ruixiang Feng and Tao Zhang and Wei Ruan and Xiaoqi Liu and Xiaoxue Cheng and Xiyun Xu and Yang Song and Yanzipeng Gao and Yiming Jia and Yun Xing and Yuntao Wen and Zekai Wang and Zhenwei An and Zhicong Sun and Zongchao Chen},
year={2025},
eprint={2512.06266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.06266},
}
Contact
If you have any questions, please raise an issue or contact us at nanbeige@126.com.
🚀 If you find these models useful
Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:
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:
"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"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.
