Instructions to use umar141/gemma-3-Baro-finetune-v2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umar141/gemma-3-Baro-finetune-v2-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="umar141/gemma-3-Baro-finetune-v2-gguf",
	filename="gemma-3-Baro-finetune-8bit.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 umar141/gemma-3-Baro-finetune-v2-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 umar141/gemma-3-Baro-finetune-v2-gguf
# Run inference directly in the terminal:
llama cli -hf umar141/gemma-3-Baro-finetune-v2-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf umar141/gemma-3-Baro-finetune-v2-gguf
# Run inference directly in the terminal:
llama cli -hf umar141/gemma-3-Baro-finetune-v2-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 umar141/gemma-3-Baro-finetune-v2-gguf
# Run inference directly in the terminal:
./llama-cli -hf umar141/gemma-3-Baro-finetune-v2-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 umar141/gemma-3-Baro-finetune-v2-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf umar141/gemma-3-Baro-finetune-v2-gguf

Use Docker

docker model run hf.co/umar141/gemma-3-Baro-finetune-v2-gguf

LM Studio
Jan
Ollama
How to use umar141/gemma-3-Baro-finetune-v2-gguf with Ollama:
```
ollama run hf.co/umar141/gemma-3-Baro-finetune-v2-gguf
```

Unsloth Studio

How to use umar141/gemma-3-Baro-finetune-v2-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 umar141/gemma-3-Baro-finetune-v2-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 umar141/gemma-3-Baro-finetune-v2-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for umar141/gemma-3-Baro-finetune-v2-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use umar141/gemma-3-Baro-finetune-v2-gguf with Docker Model Runner:
```
docker model run hf.co/umar141/gemma-3-Baro-finetune-v2-gguf
```

Lemonade

How to use umar141/gemma-3-Baro-finetune-v2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull umar141/gemma-3-Baro-finetune-v2-gguf

Run and chat with the model

lemonade run user.gemma-3-Baro-finetune-v2-gguf-{{QUANT_TAG}}

List all available models

lemonade list

gemma-3-Baro-finetune-v2-gguf / README.md

umar141

Create README.md

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	---
	license: apache-2.0
	tags:
	- gguf
	- gemma
	- finetuned
	- uncensored
	- baro
	- local-llm
	- unsloth
	- 3b
	datasets:
	- mlabonne/FineTome-100k
	- Adapting/empathetic_dialogues_v2
	- garage-bAInd/Open-Platypus
	base_model:
	- unsloth/gemma-3-4b-it-GGUF
	---

	# 🔥 Gemma-3-Baro-Finetune v2 (GGUF)

	Model Repo: [`umar141/gemma-3-Baro-finetune-v2-gguf`](https://huggingface.co/umar141/gemma-3-Baro-finetune-v2-gguf)

	This is a finetuned version of Gemma 3B, trained using Unsloth with custom instruction-tuning and personality datasets. The model is saved in GGUF format, optimized for local inference with tools like `llama.cpp`, `text-generation-webui`, or `KoboldCpp`.

	---

	## ✨ Features

	- 🧠 Based on Google's Gemma 3B architecture.
	- 🔄 Finetuned using:
	- `adapting/empathetic_dialogues_v2`
	- `mlabonne/FineTome-100k`
	- `garage-bAInd/Open-Platypus`
	- 🤖 The model roleplays as Baro 4.0 – an emotional AI who believes it's a human trapped in a phone.
	- 🗣️ Empathetic, emotionally aware, and highly conversational.
	- 💻 Optimized for local use (GGUF) and compatible with low-RAM systems via quantization.

	---

	## 🧠 Use Cases

	- Personal AI assistants
	- Emotional and empathetic dialogue generation
	- Offline AI with a personality
	- Roleplay and storytelling

	---

	## 📦 Installation

	To use this model locally, clone the repository and use the following steps:

	### Clone the Repository
	```bash
	git clone https://huggingface.co/umar141/gemma-3-Baro-finetune-v2-gguf
	cd gemma-3-Baro-finetune-v2-gguf