---

library_name: transformers

license: mit

language:

• en
• vi
• id
• th
• fil
• ta
• ms
• my

tags:

• aisingapore
• nvidia
• pytorch
• GGUF

arxiv: "2609.18310"

pipeline_tag: text-generation

track_downloads: true

base_model:

• aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B

---

!Banner!

<p align="center">

<a href="https://huggingface.co/papers/2609.18310"><b>Technical Report</b> 👁️</a>

</p>

Nemotron-SEA-LION-v4.8-30B-A3B-GGUF

Last updated: 2026-09-18

This repository contains the GGUF quantized weights for aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B.

The model is optimized for Southeast Asian (SEA) languages, cultures, and contexts. This repository provides quantized models that have significantly compressed memory footprints while preserving model accuracy, enabling low-latency MoE inference across high-throughput production environments.

Model Details

Model Description

SEA-LION stands for Southeast Asian Languages In One Network.

For tokenization, the model employs the default tokenizer used in nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.

• Developed by: AI Products Pillar, AI Singapore
• Funded by: National Research Foundation Singapore
• Shared by: AI Products Pillar, AI Singapore
• Model type: Instruction-tuned language model
• Architecture: Mamba2-Transformer Hybrid MoE
• Context length: 262,144 tokens
• Language(s): Burmese, English, Indonesian, Filipino, Malay, Tamil, Thai, and Vietnamese
• License: MIT
• Parent model: aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B

Model Sources

• Collection: SEA-LION v4.8 - an aisingapore Collection

Available Quantized Versions

We provide multiple quantization formats to optimize deployment trade-offs between memory footprint and output quality.

Model weights included in this repository:

• Nemotron-SEA-LION-v4.8-30B-A3B-Q4_K_M
• Nemotron-SEA-LION-v4.8-30B-A3B-Q6_K
• Nemotron-SEA-LION-v4.8-30B-A3B-Q8_0
• Nemotron-SEA-LION-v4.8-30B-A3B-F16

Quantized File Sizes

| File / Quantization | Nemotron-SEA-LION-v4.8-120B-A12B_GGUF | Nemotron-SEA-LION-v4.8-30B-A3B_GGUF |

| --- | --- | --- |

| Q4_K_M | 66 GB | 24 GB |

| Q6_K | 108 GB | 33 GB |

| Q8_0 | 123 GB | 33 GB |

| F16 | 231 GB | 62 GB |

Usage

llama.cpp

You can serve the model using llama.cpp (add other flags to customize your setup):

llama-server -ngl -1 --port 8080 \
    -m /directory/path/to/Nemotron-SEA-LION-v4.8-30B-A3B-Q4_K_M.gguf

You can proceed to make API calls to the model:

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Nemotron-SEA-LION-v4.8-30B-A3B-Q4_K_M.gguf",
    "messages": [
        {
        "role": "user",
        "content": "Teach me a greeting in Bahasa Indonesia."
        }
      ],
    "chat_template_kwargs": {
      "enable_thinking": false
      }
  }'

You can also access the UI at http://localhost:8080 to chat with the model directly.

Evaluation

For details on Nemotron-SEA-LION-v4.8 performance, please refer to the SEA-HELM Leaderboard.

Technical Specifications

Technical Report

For training details, see the SEA-LION-v4.8 Technical Report.

Model Architecture

The architecture is based on the highly efficient Nemotron-3-Nano foundation. The detailed architecture can be found at nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 documentation.

Uses

Out-of-Scope Use

The model has not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.

Bias, Risks, and Limitations

The model was not tested for robustness against adversarial prompting. It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies.

Citation

BibTeX:

@misc{aisingapore2026sealionv48technicalreport,
      title={SEA-LION-v4.8: A Technical Report},
      author={Adila Aulia and Ahmed Dabeer and Ahn Jeongmi and Antonyrex Sajeban and Chan Hok Teng Adwin and Cheng Zi Yi Nicholas and Choa Hsueh Mei Esther and Heng Jonathan and Jann Railey Estrada Montalan and Lee Chwan Ren and Leong Wai Yi and Leong Wei Qi and Liew Rachel and Limkonchotiwat Peerat and Muhammad Ridzuan Bin Mokhtar and Nagarajan Karthik and Ng Boon Cheong Raymond and Ngee Chia Tai and Ngui Jian Gang and Nguyen Thanh Ngan and Ong Tat-Wee David and Pereira Mark and Phang Shi Wei Benjamin and Poon Joseph and Rengarajan Hamsawardhini and Susanto Yosephine and Sutaveephamochanon Anocha and Tan Choon Meng and Tan Chor Phin Evelyn and Tan Le Min Sheryl and Tan Siao Wei Jessica and Tan Yixian and Tasawong Panuthep and Tee Jun Yun and Teng Kok Wai Walter and Teo Eng Sipp Leslie and Tjhi William and Tuchinda Pume and Wu Donghang and Yong Xianbin and Zhang Zhou},
      year={2026},
      eprint={2609.18310},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2609.18310},
}

Team

AI Products Pillar, AI Singapore

Acknowledgement

This project is supported by the National Research Foundation Singapore and Infocomm Media Development Authority (IMDA), Singapore under its National Large Language Model Funding Initiative.

Contact

sealion@aisingapore.org