Instructions to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF 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 "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF" \
    --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": "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF",
		"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 "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF" \
        --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": "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

aashish1904 commited on Sep 4, 2024

Commit

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1 Parent(s): b1b0999

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	@@ -0,0 +1,73 @@

+---
+library_name: transformers
+license: apache-2.0
+base_model: mistralai/Mistral-Nemo-Instruct-2407
+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
+---
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+# QuantFactory/Linkbricks-Horizon-AI-Korean-Advanced-12B-GGUF
+This is quantized version of [Saxo/Linkbricks-Horizon-AI-Korean-Advanced-12B](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Advanced-12B) created using llama.cpp
+# Original Model Card
+# Model Card for Model ID
+<div align="center">
+<img src="https://www.linkbricks.com/wp-content/uploads/2022/03/%E1%84%85%E1%85%B5%E1%86%BC%E1%84%8F%E1%85%B3%E1%84%87%E1%85%B3%E1%84%85%E1%85%B5%E1%86%A8%E1%84%89%E1%85%B3%E1%84%85%E1%85%A9%E1%84%80%E1%85%A9-2-1024x804.png" />
+</div>
+AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성(Saxo) 이사가 <br>
+Mistral-Nemo-Instruct-2407 베이스모델을 사용해서 H100-80G 8개를 통해 CPT(Continue-Pretraining)->SFP->DPO 한 한글 언어 모델<br>
+천만건의 한글 뉴스 코퍼스를 기준으로 다양한 테스크별 한국어-중국어-영어-일본어 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>
+-한글 Function Call 및 Tool Calling 지원 <br>
+-Deepspeed Stage=3, rslora 및 BAdam Layer Mode 사용 <br><br><br>
+Finetuned by Mr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics <br>
+CPT(Continue-Pretraining)->SFP->DPO training model based on Mistral-Nemo-Instruct-2407 through 8 H100-80Gs as a Korean language model <br>
+It is a model that has been trained to handle Korean-Chinese-English-Japanese cross-training data and 10M korean 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, amth and decision making<br>
+-128k-Context Window<br>
+-Support for Korean Functioncall and Tool Calling<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>