Nanbeige/Nanbeige4.3-33B-Base

1. Introduction

In Nanbeige4.3, we take a foundational step toward pushing the boundaries of lightweight agents. Our strategy centers on building a high-capacity teacher agent first, which will subsequently anchor the distillation of advanced agentic capabilities into smaller models.

To this end, we introduce Nanbeige4.3-33B, designed as the flagship teacher model for the Nanbeige4.3 series. Delivering state-of-the-art agentic performance among open models in the ~30B parameter class, Nanbeige4.3-33B excels at complex coding and professional cowork tasks. It not only offers a powerful, cost-effective option for immediate real-world deployment, but also lays the necessary foundation to power our upcoming small model distillation pipeline (e.g., for Nanbeige4.3-3B).

Model	STEM Reasoning			Mathematics & Coding			General Knowledge
	MMLU-Pro	GPQA	SuperGPQA	GSM8K	Math	MBPP	MMLU	CMMLU
Gemma4-31B-Base	59.3	41.5	33.8	88.9	43.6	72.5	84.2	76.9
Nanbeige4.3-33B-Base	66.9	62.0	45.6	92.9	57.3	74.6	85.5	86.8

This repository contains Nanbeige4.3-33B-Base, the base model of Nanbeige4.3-33B. Compared with Gemma4-31B-Base, which is the only frontier open-source base model released at the 30B-parameter scale in the past six months, Nanbeige4.3-33B-Base consistently wins across a broad range of benchmarks. For usecase, Nanbeige4.3-33B-Base provides a strong foundation for post-training, whether to develop general-purpose or domain-specialized agents.

2. Quickstart

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
  'Nanbeige/Nanbeige4.3-33B-Base',
  use_fast=False,
  trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
  'Nanbeige/Nanbeige4.3-33B-Base',
  torch_dtype='auto',
  device_map='auto',
  trust_remote_code=True
)

prompt = "The capital of China is"
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output_ids = model.generate(input_ids.to(model.device))
resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)
print(resp)

SGLang

MODEL_PATH=/path/to/your/Nanbeige4.3-33B-Base
python -m sglang.launch_server \
  --model-path ${MODEL_PATH} \
  --host 0.0.0.0 \
  --port 8000 \
  --tp-size 4 \
  --mem-fraction-static 0.8

vLLM

MODEL_PATH=/path/to/your/Nanbeige4.3-33B-Base
vllm serve ${MODEL_PATH} \
  --host 0.0.0.0 \
  --port 8000 \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.8

3. Contact

If you have any questions, please open an issue in this repository or contact us at nanbeige@kanzhun.com.

We also welcome collaborations to build domain-specific and industry-focused agents, including joint work on data and training.

4. Citation

If you find our model useful or would like to use it in your own work, please cite this Hugging Face project:

@misc{nanbeige43base2026,
  title        = {Nanbeige4.3-33B-Base},
  author       = {Nanbeige Team},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base}}
}