Instructions to use joongi007/ko-llama-3.1-5b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="joongi007/ko-llama-3.1-5b-instruct-GGUF",
	filename="ko-llama-3.1-5b-instruct-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use joongi007/ko-llama-3.1-5b-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 joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf joongi007/ko-llama-3.1-5b-instruct-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 joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf joongi007/ko-llama-3.1-5b-instruct-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 joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf joongi007/ko-llama-3.1-5b-instruct-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 joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joongi007/ko-llama-3.1-5b-instruct-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": "joongi007/ko-llama-3.1-5b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M

Ollama
How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with Ollama:
```
ollama run hf.co/joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use joongi007/ko-llama-3.1-5b-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 joongi007/ko-llama-3.1-5b-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 joongi007/ko-llama-3.1-5b-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 joongi007/ko-llama-3.1-5b-instruct-GGUF to start chatting

How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use joongi007/ko-llama-3.1-5b-instruct-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 joongi007/ko-llama-3.1-5b-instruct-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 joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf joongi007/ko-llama-3.1-5b-instruct-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 "joongi007/ko-llama-3.1-5b-instruct-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"

Docker Model Runner
How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use joongi007/ko-llama-3.1-5b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull joongi007/ko-llama-3.1-5b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ko-llama-3.1-5b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

Original model is kikikara/ko-llama-3.1-5b-instruct - 675085e
quantized using llama.cpp - b3716

Prompt(LM Studio)

<|start_header_id|>system<|end_header_id|>

{System}
<|eot_id|><|start_header_id|>user<|end_header_id|>

{User}
<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{Assistant}

Example(`Q4_K_M`)

system : 당신은 한국어 ai 모델입니다.
user : 왜 살아야 하는지 철학적 측면에서 접근해봐
assistant : 살아야 하는 이유에 대한 질문은 매우 깊고 복잡한 문제로, 철학적 관점에서도 여러 가지 해석이 존재합니다. 여기 몇 가지 주요 논점을 소개하겠습니다.

1. **개인적 의미와 목표**: 각 개인의 삶에는 고유한 의미와 목표가 있습니다. 어떤 사람에게는 사랑과 관계가, 다른 사람은 직업적 성취나 개인적 성장, 혹은 인류 전체에 기여하는 것이 삶의 중요한 부분일 수 있습니다.

2. **생명과 죽음 사이의 경계**: 인간은 생명과 죽음 사이에서 살아가는 존재입니다. 어떤 사람들은 자신의 삶에서 의미를 찾는다면 그들이 목숨을 두고 살아야 할 수도 있으며, 이는 단순히 생물학적인 이유뿐만 아니라 철학적 관점에서도 중요한 문제로 다루어집니다.

3. **인류의 발전과 기여**: 인간은 인류의 발전에 기여할 수 있는 존재입니다. 그러므로 자신의 삶에서 이에 기여하는 것은 그 자체로 매우 중요한 일일 수 있습니다.

4. **삶의 의미와 목적**: 철학자들은 종종 "삶의 의미"나 "삶의 목적"을 탐구합니다. 어떤 사람들은 사랑이나 영혼의 안정, 혹은 인류의 발전에 기여하는 것이 삶의 중요한 요소로 간주될 수 있습니다.

이러한 논점들은 철학적 탐구에서 인간 존재의 의미와 목표를 이해하는 데 도움을 줄 수 있으며, 각 개인의 삶에서 무엇이 진정으로 중요하게 여겨지는지를 고민하게 할 것입니다.<|eom_id|>

Downloads last month: 171

GGUF

Model size

6B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for joongi007/ko-llama-3.1-5b-instruct-GGUF

Base model

kikikara/ko-llama-3.1-5b-instruct

Quantized

(1)

this model

Collection including joongi007/ko-llama-3.1-5b-instruct-GGUF

MY-GGUF

Collection

11 items • Updated Oct 7, 2024

Prompt(LM Studio)

Example(Q4_K_M)

Model tree for joongi007/ko-llama-3.1-5b-instruct-GGUF

Collection including joongi007/ko-llama-3.1-5b-instruct-GGUF

Example(`Q4_K_M`)