Instructions to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF",
	filename="exaone-4.0-1.2b-float-right-tagger.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-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 FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-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 FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with Ollama:
```
ollama run hf.co/FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M
```

Unsloth Studio new

How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-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 FloatDo/exaone-4.0-1.2b-float-right-tagger-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 FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF to start chatting

Pi new

How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-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": "FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FloatDo/exaone-4.0-1.2b-float-right-tagger-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 FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with Docker Model Runner:
```
docker model run hf.co/FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M
```

Lemonade

How to use FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FloatDo/exaone-4.0-1.2b-float-right-tagger-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.exaone-4.0-1.2b-float-right-tagger-GGUF-Q4_K_M

List all available models

lemonade list

EXAONE-4.0-1.2B Float:Right Tagger (GGUF)

GGUF builds for EXAONE-4.0-1.2B Float:Right Tagger.

This model is tuned to output tags as a JSON array for short Korean text inputs.

non-GGUF : https://huggingface.co/FloatDo/exaone-4.0-1.2b-float-right-tagger

Files

exaone-4.0-1.2b-float-right-tagger.f16.gguf (FP16)
exaone-4.0-1.2b-float-right-tagger.Q8_0.gguf
exaone-4.0-1.2b-float-right-tagger.Q4_K_M.gguf ✅ recommended (often outputs clean tags with less extra text)

Prompt format (llama.cpp)

Example:

llama-cli -m exaone-4.0-1.2b-float-right-tagger.Q4_K_M.gguf -n 96 -t 8 --temp 0 \
  -p $'너는 태그 생성기다. 반드시 JSON 배열만 출력한다. 다른 글자 금지.\n\
규칙:\n- 태그 3~10개\n- 너무 디테일하지 않게(큰 주제)\n- 언더스코어(_) 금지\n\n\
문장:\n직장 상사가 계속 야근을 시켜서 스트레스 받는다. 퇴사 고민 중.\n\nassistant:\n'

Expected output (example):

["직장","스트레스","퇴사","상사","일자리"]

Notes • For stricter JSON-only behavior, use --temp 0 and keep -n small (64~96).

Downloads last month: 7

GGUF

Model size

1B params

Architecture

exaone4

Hardware compatibility

4-bit

8-bit

16-bit

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