Nanbeige4.3-33B-INT4

This repository contains the INT4 quantized version of Nanbeige4.3-33B. To preserve performance at 4-bit precision, we apply quantization-aware training (QAT), including on-policy distillation (OPD) from the BF16 model to the INT4 model and reinforcement learning (RL) on the INT4 model itself.

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 first 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).

2. Model Performance

The following BF16 results compare Nanbeige4.3-33B with recently released frontier models at around the 30B-parameter scale: Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B. All models are evaluated at their highest thinking effort. For each benchmark, we use the same inference hyperparameters and harness across models.

¹ Nanbeige4.3-33B is evaluated with preserve_thinking=true in the chat template.
² Terminal-Bench 2.1 is evaluated using Terminus 2.
³ Terminal-Bench 4 and DeepSWE are evaluated using mini-swe-agent v2.4.6.
⁴ SWE-Atlas-QnA is evaluated using mini-swe-agent v2.4.6. Answers are evaluated against 1,304 task-specific rubrics using GPT-6 Sol as the judge, with medium reasoning effort.
⁵ NL2Repo is evaluated using Claude Code.
⁶ Automation Bench v1.0.6 includes 600 publicly released tasks.
⁷ JobBench is evaluated with the Claude Code harness equipped with a search tool.
⁸ GDPval is scored using revised rubrics.
⁹ Claw-Eval is evaluated exclusively on the Claw-Eval General subset (comprising 157 single-turn user interaction tasks).
¹⁰ Toolathlon-Verified sets no max_tokens limit for individual model requests.
¹¹ YC-bench retains only the most recent 10 turns of user dialogue as historical context before each model call.
¹² DeepPlanning, EnterpriseOps-Gym, Tau3-Bench are evaluated using their official repositories.

The BF16 model leads on most of the benchmarks, showing strong performance in both agentic coding and cowork tasks.

3. Quickstart

The tokenizer provides a configurable chat template for reasoning and tool-use scenarios:

• enable_thinking controls whether the model generates reasoning for the current response. It is enabled by default; set it to False for non-thinking mode.
• preserve_thinking controls whether reasoning from previous assistant turns is retained in a multi-turn conversation. We recommend False for general chat and question answering, and True for multi-turn tool use, office tasks, and code-agent workflows.
• Passing tools enables the tool-use template. We recommend tool_call_format="xml" for the best tool-calling performance; json is also supported for compatibility.

The model supports a context length of up to 524,288 tokens (512K).

We recommend adjusting the inference settings according to the target scenario:

You can run the model with SGLang or vLLM:

SGLang

MODEL_PATH=/path/to/your/Nanbeige4.3-33B-INT4
python -m sglang.launch_server \
  --model-path ${MODEL_PATH} \
  --host 0.0.0.0 \
  --port 8000 \
  --tp-size 4 \
  --mem-fraction-static 0.8 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

vLLM

MODEL_PATH=/path/to/your/Nanbeige4.3-33B-INT4
vllm serve ${MODEL_PATH} \
  --host 0.0.0.0 \
  --port 8000 \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.8 \
  --enable-auto-tool-choice \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder

4. 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.

5. Citation

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

@misc{nanbeige43int42026,
  title        = {Nanbeige4.3-33B-INT4},
  author       = {Nanbeige Team},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B-INT4}}
}