Instructions to use jjjssjs/Llama-VARCO-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jjjssjs/Llama-VARCO-8B-Instruct-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 jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16

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 jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16

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 jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16

Use Docker

docker model run hf.co/jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16

LM Studio
Jan
Ollama
How to use jjjssjs/Llama-VARCO-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16
```

Unsloth Studio

How to use jjjssjs/Llama-VARCO-8B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jjjssjs/Llama-VARCO-8B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jjjssjs/Llama-VARCO-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jjjssjs/Llama-VARCO-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use jjjssjs/Llama-VARCO-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16
```

Lemonade

How to use jjjssjs/Llama-VARCO-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jjjssjs/Llama-VARCO-8B-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.Llama-VARCO-8B-Instruct-GGUF-F16

List all available models

lemonade list

Atomic Chat

Llama-VARCO-8B-Instruct-GGUF

Thanks to https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct

Transcribe the original text

Llama-VARCO-8B-Instruct

About the Model

Llama-VARCO-8B-Instruct is a generative model built with Llama, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.

Developed by: NC Research, Language Model Team
Languages (NLP): Korean, English
License: LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
Base model: meta-llama/Meta-Llama-3.1-8B

LogicKor

We used the LogicKor code to measure performance. For the judge model, we used the officially recommended gpt-4-1106-preview. The score includes only the 0-shot evaluation provided in the default.

Model	Math	Reasoning	Writing	Coding	Understanding	Grammer	Single turn	Multi turn	Overall
Llama-VARCO-8B-Instruct	6.71 / 8.57	8.86 / 8.29	9.86 / 9.71	8.86 / 9.29	9.29 / 10.0	8.57 / 7.86	8.69	8.95	8.82
EXAONE-3.0-7.8B-Instruct	6.86 / 7.71	8.57 / 6.71	10.0 / 9.29	9.43 / 10.0	10.0 / 10.0	9.57 / 5.14	9.07	8.14	8.61
Meta-Llama-3.1-8B-Instruct	4.29 / 4.86	6.43 / 6.57	6.71 / 5.14	6.57 / 6.00	4.29 / 4.14	6.00 / 4.00	5.71	5.12	5.42
Gemma-2-9B-Instruct	6.14 / 5.86	9.29 / 9.0	9.29 / 8.57	9.29 / 9.14	8.43 / 8.43	7.86 / 4.43	8.38	7.57	7.98
Qwen2-7B-Instruct	5.57 / 4.86	7.71 / 6.43	7.43 / 7.00	7.43 / 8.00	7.86 / 8.71	6.29 / 3.29	7.05	6.38	6.71

Downloads last month: 20

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit

View +1 variant

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