Instructions to use Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo with Transformers:

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

pipe = pipeline("text-generation", model="Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo")
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-Llama-3.3-Korean-70B-sft-dpo")
model = AutoModelForCausalLM.from_pretrained("Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo 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-Llama-3.3-Korean-70B-sft-dpo"
# 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-Llama-3.3-Korean-70B-sft-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo

SGLang

How to use Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo 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-Llama-3.3-Korean-70B-sft-dpo" \
    --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-Llama-3.3-Korean-70B-sft-dpo",
		"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-Llama-3.3-Korean-70B-sft-dpo" \
        --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-Llama-3.3-Korean-70B-sft-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo with Docker Model Runner:
```
docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo
```

Model Card for Model ID

AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성(Saxo) 이사가
meta-llama/Llama-3.3-70B-Instruct 베이스모델을 사용해서 H100-80G 8개를 통해 한국어 SFT->DPO 한 한국어 강화 언어 모델
4천만건의 한글 뉴스 및 위키 코퍼스를 기준으로 다양한 테스크별 한국어-일본어-중국어-영어 교차 학습 데이터와 수학 및 논리판단 데이터를 통하여 한중일영 언어 교차 증강 처리와 복잡한 논리 문제 역시 대응 가능하도록 훈련한 모델이다.
-토크나이저는 단어 확장 없이 베이스 모델 그대로 사용
-고객 리뷰나 소셜 포스팅 고차원 분석 및 코딩과 작문, 수학, 논리판단 등이 강화된 모델
-128k-Context Window
-Function Call 및 Tool Calling 지원
-128k-Context Window
-Deepspeed Stage=3, rslora 및 BAdam Layer Mode 사용
-"transformers_version": "4.46.3"

Finetuned by Mr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics
Korean SFT->DPO training model based on Saxo/Linkbricks-Horizon-AI-Japanese-Base-70B through 8 H100-80Gs as a Korean boosting language model
It is a model that has been trained to handle Korean-Japanese-Chinese-English cross-training data and 40M Korean news corpus and logic judgment data for various tasks to enable cross-fertilization processing and complex Korean logic & math problems.
-Tokenizer uses the base model without word expansion
-Models enhanced with high-dimensional analysis of customer reviews and social posts, as well as coding, writing, math and decision making
-Function Calling
-128k-Context Window
-Deepspeed Stage=3, use rslora and BAdam Layer Mode

www.horizonai.ai, www.linkbricks.com, www.linkbricks.vc