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

Libraries

How to use aisingapore/Llama-SEA-LION-v3-8B 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")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aisingapore/Llama-SEA-LION-v3-8B")
model = AutoModelForCausalLM.from_pretrained("aisingapore/Llama-SEA-LION-v3-8B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aisingapore/Llama-SEA-LION-v3-8B 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"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisingapore/Llama-SEA-LION-v3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aisingapore/Llama-SEA-LION-v3-8B

SGLang

How to use aisingapore/Llama-SEA-LION-v3-8B 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" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisingapore/Llama-SEA-LION-v3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisingapore/Llama-SEA-LION-v3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

tainc commited on Dec 11, 2024

Commit

b99fd0c

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@@ -33,7 +33,7 @@ The continued pre-training data for Llama3.1 8B CPT SEA-LIONv3 base model encomp
 - **Funded by:** Singapore NRF
 - **Model type:** Decoder
 - **Languages:** English, Chinese, Vietnamese, Indonesian, Thai, Filipino, Tamil, Malay, Khmer, Lao, Burmese, Javanese, Sundanese
-- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
 For tokenisation, the model employs the default tokenizer used in Llama3.1 8B Instruct.
@@ -93,7 +93,7 @@ Llama3.1 8B CPT SEA-LIONv3 base model was continued pre-trained on 200B tokens o
 Note:
-- All token counts are counted using Gemma2 tokenizer
 - Wiki* sources includes Wikipedia, Wiki Books, Wiki Source, Wiki Voyage and Fandom Wiki
 - News* sources includes VOA, Global Voices, MediaCorp, VinBigData-News
 - Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/)

 - **Funded by:** Singapore NRF
 - **Model type:** Decoder
 - **Languages:** English, Chinese, Vietnamese, Indonesian, Thai, Filipino, Tamil, Malay, Khmer, Lao, Burmese, Javanese, Sundanese
+- **License:** [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
 For tokenisation, the model employs the default tokenizer used in Llama3.1 8B Instruct.
 Note:
+- All token counts are counted using Llama 3.1 tokenizer
 - Wiki* sources includes Wikipedia, Wiki Books, Wiki Source, Wiki Voyage and Fandom Wiki
 - News* sources includes VOA, Global Voices, MediaCorp, VinBigData-News
 - Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/)