Instructions to use aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base

SGLang

How to use aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base with Docker Model Runner:
```
docker model run hf.co/aisingapore/Nemotron-SEA-LION-v4.8-30B-A3B-Base
```

Technical Report 👁️

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

Last updated: 2026-09-18

SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asian (SEA) region. Nemotron-SEA-LION-v4.8-30B-A3B-Base is built upon the nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 architecture. This model underwent continued pretraining on 150B high-quality tokens across QA, reasoning, code, and translation data using the NVIDIA NeMo Megatron Bridge library.

Model Details

Model Description

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

We performed CPT for 150B tokens in English and 10 SEA languages.

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

Developed by: AI Products Pillar, AI Singapore
Funded by: National Research Foundation Singapore
Shared by: AI Products Pillar, AI Singapore
Model type: Base language model
Architecture: LatentMoE Hybrid
Context length: 262,144 tokens
Language(s): Balinese, Burmese, English, Indonesian, Javanese, Khmer, Lao, Malay, Mandarin, Sundanese, Tamil, Thai, and Vietnamese
License: MIT
Parent model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16

Model Sources

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

Training Details

Training Data

The continued pretraining dataset comprised 150B tokens on QA, CoT, Reasoning and parallel (translation) datasets adapted from

Training Regime

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

Environmental Impact

Hardware type: H200
GPU-hours: 114.23
Cloud provider: SMC H200
Compute region: Singapore
Carbon emissions: approximately 0.003 – 0.062 MT

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-Base-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.