Instructions to use Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b with Transformers:

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

pipe = pipeline("text-generation", model="Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b")
model = AutoModelForCausalLM.from_pretrained("Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b")
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 Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b

SGLang

How to use Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b 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 "Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b" \
    --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": "Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b",
		"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 "Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b" \
        --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": "Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b with Docker Model Runner:
```
docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Korean-LLAMA3blend-8x8b
```

Model Card for Model ID

AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성(Saxo) 이사가 meta-llama/Meta-Llama-3-8B를 베이스모델로 GCP상의 H100-80G 8개를 통해 SFT-DPO 훈련한 한글 기반 LLAMA3-8b 8개의 MoE(Mixture of Expert)모델. 토크나이저는 라마3랑 동일하며 한글 VOCA 확장은 하지 않은 버전 입니다. 일반질의응답(채팅)-의료-군사-한중일번역-코딩 각 특화 LLM을 통합

Dr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics, trained the meta-llama/Meta-Llama-3-8B base model on 8 H100-60Gs on GCP for 4 hours of instructional training (8000 Tokens). Accelerate, Deepspeed Zero-3 libraries were used.

www.linkbricks.com, www.linkbricks.vc