Instructions to use minseokk7/monarda-g4-e4b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minseokk7/monarda-g4-e4b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="minseokk7/monarda-g4-e4b-gguf",
	filename="monarda-g4-e4b-base-q8_0.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 minseokk7/monarda-g4-e4b-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 minseokk7/monarda-g4-e4b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf minseokk7/monarda-g4-e4b-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf minseokk7/monarda-g4-e4b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf minseokk7/monarda-g4-e4b-gguf:Q8_0

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 minseokk7/monarda-g4-e4b-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf minseokk7/monarda-g4-e4b-gguf:Q8_0

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 minseokk7/monarda-g4-e4b-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf minseokk7/monarda-g4-e4b-gguf:Q8_0

Use Docker

docker model run hf.co/minseokk7/monarda-g4-e4b-gguf:Q8_0

LM Studio
Jan

vLLM

How to use minseokk7/monarda-g4-e4b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minseokk7/monarda-g4-e4b-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": "minseokk7/monarda-g4-e4b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/minseokk7/monarda-g4-e4b-gguf:Q8_0

Ollama
How to use minseokk7/monarda-g4-e4b-gguf with Ollama:
```
ollama run hf.co/minseokk7/monarda-g4-e4b-gguf:Q8_0
```

Unsloth Studio

How to use minseokk7/monarda-g4-e4b-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 minseokk7/monarda-g4-e4b-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 minseokk7/monarda-g4-e4b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for minseokk7/monarda-g4-e4b-gguf to start chatting

How to use minseokk7/monarda-g4-e4b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf minseokk7/monarda-g4-e4b-gguf:Q8_0

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": "minseokk7/monarda-g4-e4b-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use minseokk7/monarda-g4-e4b-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 minseokk7/monarda-g4-e4b-gguf:Q8_0

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 minseokk7/monarda-g4-e4b-gguf:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use minseokk7/monarda-g4-e4b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf minseokk7/monarda-g4-e4b-gguf:Q8_0

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 "minseokk7/monarda-g4-e4b-gguf:Q8_0" \
  --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 minseokk7/monarda-g4-e4b-gguf with Docker Model Runner:
```
docker model run hf.co/minseokk7/monarda-g4-e4b-gguf:Q8_0
```

Lemonade

How to use minseokk7/monarda-g4-e4b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull minseokk7/monarda-g4-e4b-gguf:Q8_0

Run and chat with the model

lemonade run user.monarda-g4-e4b-gguf-Q8_0

List all available models

lemonade list

Monarda G4 E4B GGUF

Monarda is an offline Korean-English translation post-editor for Liquid Translate. It reads the original text and a Bergamot draft, then returns only the corrected translation.

Files

monarda-g4-e4b-base-q8_0.gguf: Q8_0 conversion of google/gemma-4-E4B-it for llama.cpp.
monarda-translation-correction-focus-f16.gguf: Monarda translation correction LoRA adapter.

Both files are required. Load the base with the adapter:

llama-server -m monarda-g4-e4b-base-q8_0.gguf \
  --lora monarda-translation-correction-focus-f16.gguf \
  --ctx-size 8192

Intended use

English to Korean and Korean to English translation correction.
Offline use after the files have been downloaded.
Post-editing a machine-translation draft while preserving names, numbers, formatting, and technical terms.

The model is not intended to provide factual, legal, medical, or safety-critical advice. Users should review important translations.

Training and data

The adapter was trained with direct target-only supervision. Abstract-CoT-style representations were used during the development and teacher-data workflow, but reasoning traces are not output labels and the released model is instructed to return translation text only.

Training sources used for the releasable model include:

Tatoeba bilingual sentence pairs distributed through OPUS under CC BY 2.0 FR.
Project-authored technical terminology and correction templates under CC0 1.0.

AI Hub and DeepL samples were used only for held-out comparison/evaluation and were not included as training targets in this release.

Evaluation snapshot

Correction strict checks: 12/12
General core-meaning checks: 12/12
Technical core-meaning checks: 12/12
Empty output, trace leakage, and source echo: 0 in the recorded suites
Technical reference similarity: 0.8779 for the selected adapter versus 0.8302 for the base candidate

These are small project evaluation suites and must not be interpreted as a comprehensive benchmark.

Checksums

05a9b734d32688c4f9c00bd16f88109dcfbf33d63da9645f11f2a9375dcbbb0c  monarda-g4-e4b-base-q8_0.gguf
14fda53b2d83b30db930f9deb19838b4196f878a80c3226a2dfb45771f822b05  monarda-translation-correction-focus-f16.gguf

License and attribution

The base model is Google Gemma 4 E4B IT, released under Apache License 2.0. This repository is released under Apache License 2.0. Tatoeba-derived training pairs require their original attribution under CC BY 2.0 FR.

llama.cpp is not included in this model repository. Liquid Translate distributes its runtime separately under the llama.cpp MIT license.

Downloads last month: 334

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

8-bit

16-bit

Model tree for minseokk7/monarda-g4-e4b-gguf

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

(298)

this model