Instructions to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF"
# 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/Llama-SEA-LION-v3-8B-IT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

SGLang

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF 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/Llama-SEA-LION-v3-8B-IT-GGUF" \
    --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/Llama-SEA-LION-v3-8B-IT-GGUF",
		"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/Llama-SEA-LION-v3-8B-IT-GGUF" \
        --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/Llama-SEA-LION-v3-8B-IT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with Ollama:
```
ollama run hf.co/aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M
```
Unsloth Desktop

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with Docker Model Runner:
```
docker model run hf.co/aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M
```

Lemonade

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-SEA-LION-v3-8B-IT-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "aisingapore/Llama-SEA-LION-v3-8B-IT-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

A newer version of this model is available: aisingapore/Qwen-SEA-LION-v4.5-27B-IT

Llama-SEA-LION-v3-8B-IT

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

Llama-SEA-LION-v3-8B-IT is a multilingual model that has been fine-tuned in two stages on approximately 12.3M English instruction-completion pairs alongside a pool of 4.5M Southeast Asian instruction-completion pairs from SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai and Vietnamese.

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

Developed by: Products Pillar, AI Singapore
Funded by: Singapore NRF
Model type: Decoder
Languages supported: Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
License: Llama 3.1 Community License

Description

This repo contains GGUF format model files for aisingapore/Llama-SEA-LION-v3-8B-IT.

Model Weights Included in this repository:

Caveats

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 in its reasoning.

Limitations

Safety

Current SEA-LION models, including this commercially permissive release, have 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 claim, damages, or other liability arising from the use of the released weights and codes.

Technical Specifications

Fine-Tuning Details

Llama-SEA-LION-v3-8B-IT was tuned using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 1024 GPU hours, on a single node of 8x H100-80GB GPUs.

Data

Llama-SEA-LION-v3-8B-IT was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.

Lineage & Versioning

This model card serves as an immutable record of the final released checkpoint. Consequently, the hardware specifications, compute hours, and precise dataset volumes (such as final filtered instruction counts) reported here reflect the exact production run used to generate this specific artifact. These figures may differ from the aggregate totals, pre-filtered data pools, or preliminary experimental runs (e.g., initial H100 benchmarks) documented in our accompanying research papers.

The training counts and dataset mixture details reported in this model card reflect the exact constructed training pool consumed during this specific model run. Figures may differ slightly from public dataset releases, which represent downloadable open-source subsets of the broader corpus.

Call for Contributions

We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.

The Team

Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin

Acknowledgements

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.

Contact

For more info, please contact us using this SEA-LION Inquiry Form

Link to SEA-LION's GitHub repository

Disclaimer

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.