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

Libraries

How to use aisingapore/Apertus-SEA-LION-v4-8B-IT with Transformers:

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

pipe = pipeline("text-generation", model="aisingapore/Apertus-SEA-LION-v4-8B-IT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aisingapore/Apertus-SEA-LION-v4-8B-IT")
model = AutoModelForCausalLM.from_pretrained("aisingapore/Apertus-SEA-LION-v4-8B-IT", 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/Apertus-SEA-LION-v4-8B-IT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisingapore/Apertus-SEA-LION-v4-8B-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/Apertus-SEA-LION-v4-8B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aisingapore/Apertus-SEA-LION-v4-8B-IT

SGLang

How to use aisingapore/Apertus-SEA-LION-v4-8B-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/Apertus-SEA-LION-v4-8B-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/Apertus-SEA-LION-v4-8B-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/Apertus-SEA-LION-v4-8B-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/Apertus-SEA-LION-v4-8B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aisingapore/Apertus-SEA-LION-v4-8B-IT with Docker Model Runner:
```
docker model run hf.co/aisingapore/Apertus-SEA-LION-v4-8B-IT
```

waiyiaisg commited on Feb 5

Commit

8b893eb

verified ·

1 Parent(s): 12aef3d

Update README.md

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README.md +4 -11

README.md CHANGED Viewed

@@ -1,12 +1,7 @@
 ---
 library_name: transformers
-tags:
-- generated_from_trainer
-model-index:
-- name: >-
-    Apertus-SEA-LION-v4-8B-IT
-  results: []
-license: apache-2.0
 language:
   - en
   - zh
@@ -16,11 +11,9 @@ language:
   - fil
   - ta
   - ms
-  - km
-  - lo
   - my
-base_model:
-- swiss-ai/Apertus-8B-Instruct-2509
 pipeline_tag: text-generation
 ---
 ![Banner!](Apertus_SEA-LIONv4.png "v4-banner-Apertus-v4")

 ---
 library_name: transformers
+base_model:
+  - swiss-ai/Apertus-8B-Instruct-2509
 language:
   - en
   - zh
   - fil
   - ta
   - ms
   - my
+license: apache-2.0
+base_model_relation: finetune
 pipeline_tag: text-generation
 ---
 ![Banner!](Apertus_SEA-LIONv4.png "v4-banner-Apertus-v4")