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

File size: 1,550 Bytes

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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