Instructions to use wnduss/multimodal_lora_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wnduss/multimodal_lora_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="wnduss/multimodal_lora_gguf",
	filename="model.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use wnduss/multimodal_lora_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 wnduss/multimodal_lora_gguf
# Run inference directly in the terminal:
llama cli -hf wnduss/multimodal_lora_gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wnduss/multimodal_lora_gguf
# Run inference directly in the terminal:
llama cli -hf wnduss/multimodal_lora_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 wnduss/multimodal_lora_gguf
# Run inference directly in the terminal:
./llama-cli -hf wnduss/multimodal_lora_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 wnduss/multimodal_lora_gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wnduss/multimodal_lora_gguf

Use Docker

docker model run hf.co/wnduss/multimodal_lora_gguf

LM Studio
Jan
Ollama
How to use wnduss/multimodal_lora_gguf with Ollama:
```
ollama run hf.co/wnduss/multimodal_lora_gguf
```

Unsloth Studio

How to use wnduss/multimodal_lora_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 wnduss/multimodal_lora_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 wnduss/multimodal_lora_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wnduss/multimodal_lora_gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use wnduss/multimodal_lora_gguf with Docker Model Runner:
```
docker model run hf.co/wnduss/multimodal_lora_gguf
```

Lemonade

How to use wnduss/multimodal_lora_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wnduss/multimodal_lora_gguf

Run and chat with the model

lemonade run user.multimodal_lora_gguf-{{QUANT_TAG}}

List all available models

lemonade list

Multimodal LoRA (GGUF Format)

이 레포지토리는 파인튜닝된 Gemma3 LoRA 모델을 GGUF 포맷으로 변환한 파일입니다. llama.cpp, LM Studio, Ollama 등 다양한 로컬 추론 도구에서 바로 사용할 수 있습니다.

File Information

Source Model: wnduss/multimodal_lora
Quantization: f16 (원본 가중치 보존)

How to use

이 GGUF 파일은 아래 도구들에서 불러와 사용할 수 있습니다:

LM Studio: 모델 파일을 다운로드 후 검색창에 파일 경로를 입력하세요.
llama.cpp: 아래 명령어로 실행할 수 있습니다:

   ./main -m model.gguf -p "입력 프롬프트"

Downloads last month: 428

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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