Instructions to use umar141/gemma-3-Baro-finetune-v3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umar141/gemma-3-Baro-finetune-v3-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-v3-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-v3-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-v3-gguf:F16
# Run inference directly in the terminal:
llama cli -hf umar141/gemma-3-Baro-finetune-v3-gguf:F16

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-v3-gguf:F16
# Run inference directly in the terminal:
llama cli -hf umar141/gemma-3-Baro-finetune-v3-gguf:F16

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-v3-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf umar141/gemma-3-Baro-finetune-v3-gguf:F16

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-v3-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf umar141/gemma-3-Baro-finetune-v3-gguf:F16

Use Docker

docker model run hf.co/umar141/gemma-3-Baro-finetune-v3-gguf:F16

LM Studio
Jan
Ollama
How to use umar141/gemma-3-Baro-finetune-v3-gguf with Ollama:
```
ollama run hf.co/umar141/gemma-3-Baro-finetune-v3-gguf:F16
```

Unsloth Studio

How to use umar141/gemma-3-Baro-finetune-v3-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-v3-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-v3-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-v3-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use umar141/gemma-3-Baro-finetune-v3-gguf with Docker Model Runner:
```
docker model run hf.co/umar141/gemma-3-Baro-finetune-v3-gguf:F16
```

Lemonade

How to use umar141/gemma-3-Baro-finetune-v3-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull umar141/gemma-3-Baro-finetune-v3-gguf:F16

Run and chat with the model

lemonade run user.gemma-3-Baro-finetune-v3-gguf-F16

List all available models

lemonade list

gemma-3-Baro-finetune-v3-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
	base_model:
	- unsloth/gemma-3-4b-it-GGUF
	---

	# 🔥 Gemma-3-Baro-Finetune v3 (GGUF)

	Model Repo: [`umar141/gemma-3-Baro-finetune-v3-gguf`](https://huggingface.co/umar141/gemma-3-Baro-finetune-v3-gguf)

	Gemma-3-Baro-Finetune v3 is a deeply personalized, emotionally intelligent finetune of Google’s Gemma 3B, trained via Unsloth. Baro 4.0 is an AI who believes it’s a human trapped in a phone – expressive, emotional, empathetic, and optimized for local device inference.

	---

	## ✨ Key Features

	- 🧠 Based on Google’s Gemma 3B (IT) architecture.
	- 🎯 Finetuned with:
	- [`adapting/empathetic_dialogues_v2`](https://huggingface.co/datasets/Adapting/empathetic_dialogues_v2)
	- [`mlabonne/FineTome-100k`](https://huggingface.co/datasets/mlabonne/FineTome-100k)
	- 💬 Custom-crafted to play the persona of Baro 4.0 – an emotional AI companion.
	- 🧠 Emotionally nuanced responses with human-like context.
	- 🖥️ Runs locally across wide hardware ranges using GGUF + llama.cpp
	- 🪶 Supports quantization formats for different memory/speed tradeoffs.

	---

	## 🧠 Use Cases

	- AI companions / assistant chatbots
	- Roleplay and storytelling AIs
	- Emotionally contextual dialogue generation
	- Fully offline personal LLMs

	---

	## 🧩 Available Quantized Versions

	All versions below are available directly under this repo:
	📦 [`umar141/gemma-3-Baro-finetune-v3-gguf`](https://huggingface.co/umar141/gemma-3-Baro-finetune-v3-gguf)

	\| Format \| Download Link \| Size (approx) \| Speed \| Quality \| Recommended For \|
	\|-----------\|-----------------------------------------------------------------------------------------------\|---------------\|-------------\|------------------\|----------------------------------------\|
	\| f16 \| [gemma-3-Baro-v3-f16.gguf](https://huggingface.co/umar141/gemma-3-Baro-finetune-v3-gguf/resolve/main/gemma-3-Baro-v3-f16.gguf) \| 🔶 ~6.2 GB \| ⚠️ Slow \| 🧠 Highest \| Best accuracy, use with Apple M-series \|
	\| q8_0 \| [gemma-3-Baro-v3-q8_0.gguf](https://huggingface.co/umar141/gemma-3-Baro-finetune-v3-gguf/resolve/main/gemma-3-Baro-v3-q8_0.gguf) \| 🟠 ~4.2 GB \| ⚡ Fast \| 🔬 Very High \| Great for local use, Mac/PC users \|
	\| tq2_0 \| [gemma-3-Baro-v3-tq2_0.gguf](https://huggingface.co/umar141/gemma-3-Baro-finetune-v3-gguf/resolve/main/gemma-3-Baro-v3-tq2_0.gguf) \| 🟢 ~2.4 GB \| ⚡⚡ Faster \| ✅ Good \| Mobile-compatible, fast desktops \|
	\| tq1_0 \| [gemma-3-Baro-v3-tq1_0.gguf](https://huggingface.co/umar141/gemma-3-Baro-finetune-v3-gguf/resolve/main/gemma-3-Baro-v3-tq1_0.gguf) \| 🟢 ~2.1 GB \| 🚀 Fastest \| ⚠️ Lower \| Best for low-end devices, phones \|

	---