Instructions to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mobilint/EXAONE-3.5-2.4B-Instruct-GGUF",
	filename="exaone-3.5-2.4b-instruct-vocab.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF
# Run inference directly in the terminal:
llama-cli -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF
# Run inference directly in the terminal:
llama-cli -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

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 mobilint/EXAONE-3.5-2.4B-Instruct-GGUF
# Run inference directly in the terminal:
./llama-cli -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

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 mobilint/EXAONE-3.5-2.4B-Instruct-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

Use Docker

docker model run hf.co/mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

LM Studio
Jan

vLLM

How to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mobilint/EXAONE-3.5-2.4B-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": "mobilint/EXAONE-3.5-2.4B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

Ollama
How to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with Ollama:
```
ollama run hf.co/mobilint/EXAONE-3.5-2.4B-Instruct-GGUF
```

Unsloth Studio new

How to use mobilint/EXAONE-3.5-2.4B-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 mobilint/EXAONE-3.5-2.4B-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 mobilint/EXAONE-3.5-2.4B-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 mobilint/EXAONE-3.5-2.4B-Instruct-GGUF to start chatting

Docker Model Runner
How to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mobilint/EXAONE-3.5-2.4B-Instruct-GGUF
```

Lemonade

How to use mobilint/EXAONE-3.5-2.4B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mobilint/EXAONE-3.5-2.4B-Instruct-GGUF

Run and chat with the model

lemonade run user.EXAONE-3.5-2.4B-Instruct-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

EXAONE 3.5 2.4B Instruct — GGUF + MXQ for llama-cli-mblt

This repository provides EXAONE 3.5 2.4B Instruct compiled and optimized for Mobilint NPU hardware, packaged for use with llama.cpp-mblt.

Branches

Branch	Contents	Description
`main`	Body model only	Standard autoregressive decoding
`eagle3`	Body + FC + Draft models	EAGLE3 speculative decoding (~2-4x faster)

Quick Start

# Simple decoding
llama-cli-mblt -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF -p "Hello!" -n 128

# EAGLE3 speculative decoding
llama-cli-mblt -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF --eagle3 -p "Hello!" -n 128

# Interactive chat
llama-cli-mblt -hf mobilint/EXAONE-3.5-2.4B-Instruct-GGUF --eagle3

Files

main branch

File	Size	Description
`exaone-3.5-2.4b-instruct-vocab.gguf`	4.0 MB	Tokenizer (vocab-only GGUF)
`target_emb.bin`	1.0 GB	Body embedding weights (float32)
`EXAONE-3.5-2.4B-Instruct.mxq`	1.4 GB	Body model for NPU
`config.json`	—	Model configuration

eagle3 branch (adds)

File	Size	Description
`single_Fc_EXAONE-3.5-2.4B-Instruct.mxq`	19 MB	FC dimension converter model
`Draft_EXAONE-3.5-2.4B-Instruct.mxq`	87 MB	EAGLE3 draft model
`draft_emb.bin`	1.0 GB	Draft embedding weights
`d2t.bin`	250 KB	Draft-to-target vocabulary mapping