Instructions to use Agnes-AI/Agnes-SeaLLM-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnes-AI/Agnes-SeaLLM-8b with Transformers:

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

pipe = pipeline("text-generation", model="Agnes-AI/Agnes-SeaLLM-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Agnes-AI/Agnes-SeaLLM-8b")
model = AutoModelForCausalLM.from_pretrained("Agnes-AI/Agnes-SeaLLM-8b", 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 Agnes-AI/Agnes-SeaLLM-8b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Agnes-AI/Agnes-SeaLLM-8b

SGLang

How to use Agnes-AI/Agnes-SeaLLM-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 "Agnes-AI/Agnes-SeaLLM-8b" \
    --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": "Agnes-AI/Agnes-SeaLLM-8b",
		"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 "Agnes-AI/Agnes-SeaLLM-8b" \
        --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": "Agnes-AI/Agnes-SeaLLM-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Agnes-AI/Agnes-SeaLLM-8b with Docker Model Runner:
```
docker model run hf.co/Agnes-AI/Agnes-SeaLLM-8b
```

Agnes-AI commited on Jan 8

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@@ -19,7 +19,7 @@ pipeline_tag: text-generation
 We introduce Agnes-SeaLLM-8B, a compact Large Language Model (LLM) meticulously optimized for Southeast Asian languages. Despite its efficient footprint, it delivers performance rivaling much larger open-source models, excelling in tasks such as mathematical reasoning, translation, and instruction following. Furthermore, it has been specially tuned to enhance reliability, minimize hallucinations, and provide culturally sensitive, safe responses tailored to the Southeast Asian context.
 ![image.png](images/image.png)
-🔥 Highlights
 Compact Efficiency & Rapid Deployment: Significantly smaller than mainstream LLMs, Agnes-SeaLLM-8B enables high-speed inference and low-resource deployment without sacrificing accuracy or multilingual proficiency. It is the ideal choice for edge devices and resource-constrained environments.
 Top-Tier Performance in its Class: Outperforms comparable open-source models across multi-dimensional benchmarks, including academic examinations, complex instruction following, mathematics, and high-precision translation.

 We introduce Agnes-SeaLLM-8B, a compact Large Language Model (LLM) meticulously optimized for Southeast Asian languages. Despite its efficient footprint, it delivers performance rivaling much larger open-source models, excelling in tasks such as mathematical reasoning, translation, and instruction following. Furthermore, it has been specially tuned to enhance reliability, minimize hallucinations, and provide culturally sensitive, safe responses tailored to the Southeast Asian context.
 ![image.png](images/image.png)
+# 🔥 Highlights
 Compact Efficiency & Rapid Deployment: Significantly smaller than mainstream LLMs, Agnes-SeaLLM-8B enables high-speed inference and low-resource deployment without sacrificing accuracy or multilingual proficiency. It is the ideal choice for edge devices and resource-constrained environments.
 Top-Tier Performance in its Class: Outperforms comparable open-source models across multi-dimensional benchmarks, including academic examinations, complex instruction following, mathematics, and high-precision translation.