Instructions to use FloatDo/EXAONE-Deep-2.4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FloatDo/EXAONE-Deep-2.4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FloatDo/EXAONE-Deep-2.4B-GGUF",
	filename="EXAONE-Deep-2.4B.F16.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-Deep-2.4B-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-Deep-2.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FloatDo/EXAONE-Deep-2.4B-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-Deep-2.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FloatDo/EXAONE-Deep-2.4B-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-Deep-2.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FloatDo/EXAONE-Deep-2.4B-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-Deep-2.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FloatDo/EXAONE-Deep-2.4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FloatDo/EXAONE-Deep-2.4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use FloatDo/EXAONE-Deep-2.4B-GGUF with Ollama:
```
ollama run hf.co/FloatDo/EXAONE-Deep-2.4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use FloatDo/EXAONE-Deep-2.4B-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-Deep-2.4B-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-Deep-2.4B-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-Deep-2.4B-GGUF to start chatting

Docker Model Runner
How to use FloatDo/EXAONE-Deep-2.4B-GGUF with Docker Model Runner:
```
docker model run hf.co/FloatDo/EXAONE-Deep-2.4B-GGUF:Q4_K_M
```

Lemonade

How to use FloatDo/EXAONE-Deep-2.4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FloatDo/EXAONE-Deep-2.4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.EXAONE-Deep-2.4B-GGUF-Q4_K_M

List all available models

lemonade list

Calvin806/EXAONE-Deep-2.4B-GGUF

GGUF quantizations for EXAONE-Deep-2.4B.

This folder typically contains:

EXAONE-Deep-2.4B.F16.gguf
EXAONE-Deep-2.4B.Q4_K_M.gguf
EXAONE-Deep-2.4B.Q5_K_M.gguf
EXAONE-Deep-2.4B.Q8_0.gguf (optional)

🔧 llama.cpp patch (EXAONE GGUF quantize compatibility)

EXAONE GGUF 변환/양자화 과정에서 일부 모델(예: 2.4B / 7.8B) 간 KV key 네이밍 불일치가 발견되었습니다.

어떤 GGUF는 exaone.attention.layer_norm_epsilon만 존재
어떤 GGUF는 exaone.attention.layer_norm_rms_epsilon만 존재

이 상태에서 vanilla llama.cpp의 llama-quantize가 특정 키를 찾지 못해 실패할 수 있어, llama.cpp의 model loader에서 gguf key lookup에 fallback을 추가하는 패치를 적용했습니다.

What was patched

src/llama-model-loader.cpp에서 gguf_find_key() lookup에 다음 fallback을 수행하도록 수정:

key가 exaone.attention.layer_norm_epsilon이고 찾지 못하면 → exaone.attention.layer_norm_rms_epsilon로 재시도
key가 exaone.attention.layer_norm_rms_epsilon이고 찾지 못하면 → exaone.attention.layer_norm_epsilon로 재시도

이 패치를 통해 EXAONE 3.5 / EXAONE-Deep 2.4B, 7.8B, 32B 계열을 동일 파이프라인으로 GGUF+quantize할 수 있습니다.

Patch note (minimal diff summary)

Added a fallback wrapper/hook for gguf_find_key() inside llama-model-loader.cpp
Ensured all lookups in that translation unit route through the fallback

This repo includes:

exaone-gguf-fallback.patch

Tested llama.cpp commit

021cc28bef4dd7d0bf9c91dbbd0803caa6cb15f2

Build (CUDA)

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
git apply ../exaone-gguf-fallback.patch

rm -rf build
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
cmake --build build -j

Convert / Quantize

# Convert HF snapshot -> GGUF(F16)
python3 llama.cpp/convert_hf_to_gguf.py <LOCAL_SNAPSHOT_DIR> --outtype f16 --outfile model.F16.gguf

# Quantize (example: Q4_K_M)
llama.cpp/build/bin/llama-quantize model.F16.gguf model.Q4_K_M.gguf Q4_K_M

Downloads last month: 13

GGUF

Model size

2B params

Architecture

exaone

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

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FloatDo
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EXAONE-Deep-2.4B-GGUF