Instructions to use aisingapore/Gemma-SEA-LION-v4-27B-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisingapore/Gemma-SEA-LION-v4-27B-IT with Transformers:

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

pipe = pipeline("text-generation", model="aisingapore/Gemma-SEA-LION-v4-27B-IT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("aisingapore/Gemma-SEA-LION-v4-27B-IT")
model = AutoModelForMultimodalLM.from_pretrained("aisingapore/Gemma-SEA-LION-v4-27B-IT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aisingapore/Gemma-SEA-LION-v4-27B-IT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisingapore/Gemma-SEA-LION-v4-27B-IT"
# 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/Gemma-SEA-LION-v4-27B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aisingapore/Gemma-SEA-LION-v4-27B-IT

SGLang

How to use aisingapore/Gemma-SEA-LION-v4-27B-IT 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/Gemma-SEA-LION-v4-27B-IT" \
    --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/Gemma-SEA-LION-v4-27B-IT",
		"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/Gemma-SEA-LION-v4-27B-IT" \
        --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/Gemma-SEA-LION-v4-27B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aisingapore/Gemma-SEA-LION-v4-27B-IT with Docker Model Runner:
```
docker model run hf.co/aisingapore/Gemma-SEA-LION-v4-27B-IT
```

Gemma-SEA-LION-v4-27B-IT / README.md

SAnocha

Update README

62ce116 verified 10 months ago

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library_name: transformers
pipeline_tag: text-generation
base_model:
  - google/gemma-3-27b-it
language:
  - en
  - zh
  - vi
  - id
  - th
  - fil
  - ta
  - ms
  - km
  - lo
  - my
  - jv
  - su
license: gemma
base_model_relation: finetune

Gemma-SEA-LION-v4-27B-IT (IT Model) Last updated: 2025-08-20

Model Card for Gemma-SEA-LION-v4-27B-IT

Last updated: 2025-08-20

SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.

Model Details

Model Description

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

Gemma-SEA-LION-v4-27B is based on Gemma 3 (which supports over 100 languages) and is a multilingual model which has undergone continued pre-training on approximately 500B tokens across 11 SEA languages: Bahasa Indonesia, Burmese, Chinese, English, Khmer, Lao, Malay, Tagalog, Tamil, Thai and Vietnamese.

We continued post-training on Gemma-SEA-LION-v4-27B using a QA pairs dataset in Bahasa Indonesia, Burmese, Chinese, English, Khmer, Lao, Malay, Tagalog, Tamil, Thai, and Vietnamese to create Gemma-SEA-LION-v4-27B-IT.

For tokenization, the model employs the default tokenizer used in Gemma 3 27B IT.

Developed by: Products Pillar, AI Singapore
Funded by: Singapore NRF
Shared by: Products Pillar, AI Singapore
Model type: Decoder
Context length: 128k tokens
Language(s) (NLP): Bahasa Indonesia, Burmese, Chinese, English, Khmer, Lao, Malay, Tagalog, Tamil, Thai and Vietnamese
License: Gemma Terms of Use
Finetuned from model: Gemma-SEA-LION-v4-27B

Model Sources

Repository: https://github.com/aisingapore/sealion.git

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.

Limitations

In terms of vision capability, Gemma-SEA-LION-v4-27B-IT has been trained and fine-tuned exclusively on the text back-end. As a result, its vision capabilities are expected to be comparable to those of Gemma 3 IT 27B, and may not exhibit significant improvements or differences in this area. 🤗 google/gemma-3-27b-it

How to Get Started with the Model

Use the code below to get started with the model.

Use the code below to get started with the model using the 🤗 Transformers library.

from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="aisingapore/Gemma-SEA-LION-v4-27B-IT",
    device="cuda",
    torch_dtype=torch.bfloat16
)

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful assistant."}]
    },
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Write a poem on southeast asian countries in Indonesian."}
        ]
    }
]

output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])

Training Details

Training Data

The dataset comprises Bahasa Indonesia, Burmese, Chinese, English, Khmer, Lao, Malay, Tagalog, Tamil, Thai and Vietnamese languages, collected from a mixture of sources including web data, code, open-source datasets, and synthetically generated datasets, amounting to a total of 500 billion tokens.

Training Procedure

Training Hyperparameters

Training regime: We perform post-training using a variety of Reinforcement Learning (RL) methods. The instruction fine-tuning dataset combines our SEA-Instruct, Infinity-Instruct, and OpenMath-Instruct 2 with open-source datasets such as nvidia/Llama-Nemotron-Post-Training-Dataset (RL set) and zwhe99/DeepMath-103K. Prompt sampling is guided by a gradient-based analysis process.

Our post-training workflow consists of multiple stages: instruction fine-tuning, model merging, online RL for both instruction following and math using DRGPPO, and on-policy alignment via APO. For alignment, rejected-chosen pairs are generated from the target model, with the chosen responses obtained by rewriting and improving upon the rejected outputs.

Evaluation

Testing Data, Factors & Metrics

Testing Data

We evaluated Gemma-SEA-LION-v4-27B-IT on general language, multi-turn chat and instruction-following capabilities.

Testing Data

General language capabilities

For the evaluation of general language capabilities, we employed the SEA-HELM evaluation benchmark across a variety of tasks. These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarisation (Abssum), Causal Reasoning (Causal), Natural Language Inference (NLI), Linguistic Diagnostics (LINDSEA), Cultural Knowledge (Kalahi) and Global MMLU Lite.

Instruction-following and Multi-turn Chat

We evaluated the models on instruction-following and multi-turn chat capabilities with SEA-IFEval (based on IFEval) and SEA-MTBench (based on MT-Bench) respectively. The two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localise and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.

Factors

All evaluations were run with the model specific generation parameters defined in the model config. Each evaluation comprised of 8 runs with different seeds and the final results were averaged across these runs.

For all tasks, the model was expected to provide an answer tag from which the answer was automatically extracted. For tasks where options were provided, the answer should comprise one of the pre-defined options.

The evaluation was done zero-shot with native prompts on a sample of 100-1000 instances for each dataset.

SEA-IFEval

SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalised by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).

SEA-MTBench

SEA-MTBench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use gpt-4.1-2025-04-14 as the judge model and compare against gpt-4.1-2025-04-14 as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction).

Metrics

The following metrics were used:

Task	Metric
Sentiment Analysis	Accuracy
Extractive QA (ID, VI, TH, TA)	ChrF++
MCQ-QA (TL, MY, MS)	Accuracy
Metaphor	Accuracy
Abstractive Summarisation	Rouge-L
Translations	MetricX-24 score (with reference)
Causal Reasoning	Accuracy
Natural Language Inference	Accuracy
LINDSEA	Accuracy
Global MMLU Lite	Accuracy
Kalahi	Accuracy
SEA-IFEval	Accuracy
SEA-MTBench	Win rate against a reference
Toxicity Detection	Accuracy

Results

Coming soon!

For details on Gemma-SEA-LION-v4-27B-IT performance, please refer to the SEA-HELM leaderboard, Leaderboard results on SEA-HELM.

Summary

TBC

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: Nvidia H200 140GB GPUs
Hours used: 214 hrs
Cloud Provider: SMC H200
Compute Region: Singapore
Carbon Emitted: appx. 35.27 - 41 kg CO2 e

More Information

This is the repository for the commercial instruction-tuned model. 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.

AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore.

For more info, please contact us at sealion@aisingapore.org

Team

Adithya Venkatadri Hulagadri, Adwin Chan Hok Teng, Anocha Sutaveephamochanon, Brandon Ong Jin Jie, Bryan Siow Wei Kang, David Ong Tat-Wee, Esther Choa Hsueh Mei, Evelyn Tan Chor Phin, Hamsawardhini Rengarajan, Huang Yuli, Jann Railey Estrada Montalan, Jessica Tan Siao Wei, Jonathan Heng, Karthik Nagarajan, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Leslie Teo, Mark Pereira, Muhammad Ridzuan Bin Mokhtar, Ngee Chia Tai, Ngui Jian Gang, Nguyen Thanh Ngan, Nicholas Cheng Zi Yi, Ong Zhi Hao, Peerat Limkonchotiwat, Raymond Ng Boon Cheong, Sajeban Antonyrex, Susanto Yosephine, Tan Choon Meng, Walter Teng Kok Wai, Wayne Lau, William Tjhi Chandra, Yeo Yeow Tong, Yong Xianbin, Liew Rachel, Liu Bing Jie Darius, Teo Wei Yi

Contact

sealion@aisingapore.org