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

umar141 commited on Apr 17, 2025

Commit

f5720e0

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1 Parent(s): 9189e22

Update README.md

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@@ -53,9 +53,9 @@ All versions below are available directly under this repo:
 | 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      |
 ---

 | 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-finetune-f16.gguf)       | 🔶 ~7.77 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-finetune-8bit.gguf)     | 🟠 ~4.13 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-finetune-tq2_0.gguf)   | 🟢 ~2.18 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-finetune-tq1_0.gguf)   | 🟢 ~2.103GB    | 🚀 Fastest | ⚠️ Lower        | Best for low-end devices, phones      |
 ---