Instructions to use Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B with Transformers:

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

pipe = pipeline("text-generation", model="Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B")
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-Japanese-Base-32B")
model = AutoModelForCausalLM.from_pretrained("Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B", 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 Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B 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-Japanese-Base-32B"
# 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-Japanese-Base-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B

SGLang

How to use Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B 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-Japanese-Base-32B" \
    --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-Japanese-Base-32B",
		"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-Japanese-Base-32B" \
        --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-Japanese-Base-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B with Docker Model Runner:
```
docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B
```

Linkbricks-Horizon-AI-Japanese-Base-32B / README.md

Saxo

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	---
	library_name: transformers
	license: apache-2.0
	base_model: Qwen/QwQ-32B-Preview
	datasets:
	- Saxo/ko_cn_translation_tech_social_science_linkbricks_single_dataset
	- Saxo/ko_jp_translation_tech_social_science_linkbricks_single_dataset
	- Saxo/en_ko_translation_tech_science_linkbricks_single_dataset_with_prompt_text_huggingface
	- Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface
	- Saxo/ko_aspect_sentiment_sns_mall_sentiment_linkbricks_single_dataset_with_prompt_text_huggingface
	- Saxo/ko_summarization_linkbricks_single_dataset_with_prompt_text_huggingface
	- Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface
	- Saxo/ko_government_qa_total_linkbricks_single_dataset_with_prompt_text_huggingface_sampled
	- Saxo/ko-news-corpus-1
	- Saxo/ko-news-corpus-2
	- Saxo/ko-news-corpus-3
	- Saxo/ko-news-corpus-4
	- Saxo/ko-news-corpus-5
	- Saxo/ko-news-corpus-6
	- Saxo/ko-news-corpus-7
	- Saxo/ko-news-corpus-8
	- Saxo/ko-news-corpus-9
	- maywell/ko_Ultrafeedback_binarized
	- youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo
	- lilacai/glaive-function-calling-v2-sharegpt
	- kuotient/gsm8k-ko
	language:
	- ko
	- en
	- jp
	- cn
	pipeline_tag: text-generation
	---

	# Model Card for Model ID

	<div align="center">
	<img src="http://www.linkbricks.com/wp-content/uploads/2024/11/fulllogo.png" />
	</div>

	AIとビッグデータ分析の専門企業であるLinkbricksのデータサイエンティストであるジ・ユンソン(Saxo)ディレクターが <br>
	Qwen/QwQ-32B-Previewベースモデルを使用し、H100-80G 8個を通じて約25%程度のパラメータを日本語CPT(Continued-Pretraining)->SFT->DPOした日本語強化言語モデル。<br>
	2千万件の日本ニュース及びウィキコーパスを基準に、様々なタスク別の日本語・韓国語・中国語・英語クロス学習データと数学及び論理判断データを通じて、日中韓英言語クロス補強処理と複雑な論理問題にも対応できるように訓練したモデルです。
	-トークナイザーは、単語拡張なしでベースモデルのまま使用します。<br>
	-カスタマーレビューやソーシャル投稿の高次元分析及びコーディングとライティング、数学、論理判断などが強化されたモデル。<br>
	-128k-Context Window<br>
	-Function Call<br>
	-128k-Context Window<br>
	-Deepspeed Stage=3、rslora及びBAdam Layer Modeを使用 <br>
	-「transformers_version」: 「4.46.3」<br>

	<br><br>

	AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성(Saxo) 이사가 <br>
	Qwen/QwQ-32B-Preview 베이스모델을 사용해서 H100-80G 8개를 통해 약 25%정도의 파라미터를 일본어 CPT(Continued-Pretraining)->SFT->DPO 한 일본어 강화 언어 모델<br>
	2천만건의 일본 뉴스 및 위키 코퍼스를 기준으로 다양한 테스크별 일본어-한국어-중국어-영어 교차 학습 데이터와 수학 및 논리판단 데이터를 통하여 한중일영 언어 교차 증강 처리와 복잡한 논리 문제 역시 대응 가능하도록 훈련한 모델이다.<br>
	-토크나이저는 단어 확장 없이 베이스 모델 그대로 사용<br>
	-고객 리뷰나 소셜 포스팅 고차원 분석 및 코딩과 작문, 수학, 논리판단 등이 강화된 모델<br>
	-128k-Context Window<br>
	-Function Call 및 Tool Calling 지원<br>
	-128k-Context Window<br>
	-Deepspeed Stage=3, rslora 및 BAdam Layer Mode 사용 <br>
	-"transformers_version": "4.46.3"<br>
	<br><br>

	Finetuned by Mr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics <br>
	about 25% of total parameters Japanese CPT(Continued-Pretraining)->SFT->DPO training model based on Qwen/QwQ-32B-Preview through 8 H100-80Gs as a Japanese boosting language model <br>
	It is a model that has been trained to handle Japanese-Korean-Chinese-English cross-training data and 20M Japanese news corpus and logic judgment data for various tasks to enable cross-fertilization processing and complex Korean logic & math problems. <br>
	-Tokenizer uses the base model without word expansion<br>
	-Models enhanced with high-dimensional analysis of customer reviews and social posts, as well as coding, writing, math and decision making<br>
	-Function Calling<br>
	-128k-Context Window<br>
	-Deepspeed Stage=3, use rslora and BAdam Layer Mode<br>
	<br><br>

	<a href="www.linkbricks.com">www.linkbricks.com</a>, <a href="www.linkbricks.vc">www.linkbricks.vc</a>