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 from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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

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)

Model Repo: umar141/gemma-3-Baro-finetune-v3-gguf

Gemma-3-Baro-Finetune v3 is a deeply personalized, emotionally intelligent finetune of Google’s Gemma 3, 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
- 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

Format	Download Link	Size (approx)	Speed	Quality	Recommended For
f16	gemma-3-Baro-v3-f16.gguf	🔶 ~7.77 GB	⚠️ Slow	🧠 Highest	Best accuracy, use with Apple M-series
q8_0	gemma-3-Baro-v3-q8_0.gguf	🟠 ~4.13 GB	⚡ Fast	🔬 Very High	Great for local use, Mac/PC users
tq2_0	gemma-3-Baro-v3-tq2_0.gguf	🟢 ~2.18 GB	⚡⚡ Faster	✅ Good	Mobile-compatible, fast desktops
tq1_0	gemma-3-Baro-v3-tq1_0.gguf	🟢 ~2.03GB	🚀 Fastest	⚠️ Lower	Best for low-end devices, phones

Downloads last month: 60

GGUF

Model size

4B params

Architecture

gemma3

Hardware compatibility

1-bit

2-bit

16-bit

View +1 variant

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Model tree for umar141/gemma-3-Baro-finetune-v3-gguf

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

unsloth/gemma-3-4b-it-GGUF

Quantized

(3)

this model

umar141
/

gemma-3-Baro-finetune-v3-gguf

🔥 Gemma-3-Baro-Finetune v3 (GGUF)

✨ Key Features

🧠 Use Cases

🧩 Available Quantized Versions

Model tree for umar141/gemma-3-Baro-finetune-v3-gguf

Datasets used to train umar141/gemma-3-Baro-finetune-v3-gguf