Instructions to use google/gemma-2-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-2-2b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/gemma-2-2b-GGUF",
	filename="2b_pt_v2.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use google/gemma-2-2b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/gemma-2-2b-GGUF
# Run inference directly in the terminal:
llama-cli -hf google/gemma-2-2b-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/gemma-2-2b-GGUF
# Run inference directly in the terminal:
llama-cli -hf google/gemma-2-2b-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 google/gemma-2-2b-GGUF
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-2-2b-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 google/gemma-2-2b-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-2-2b-GGUF

Use Docker

docker model run hf.co/google/gemma-2-2b-GGUF

LM Studio
Jan
Ollama
How to use google/gemma-2-2b-GGUF with Ollama:
```
ollama run hf.co/google/gemma-2-2b-GGUF
```

Unsloth Studio new

How to use google/gemma-2-2b-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 google/gemma-2-2b-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 google/gemma-2-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for google/gemma-2-2b-GGUF to start chatting

Docker Model Runner
How to use google/gemma-2-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/google/gemma-2-2b-GGUF
```

Lemonade

How to use google/gemma-2-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/gemma-2-2b-GGUF

Run and chat with the model

lemonade run user.gemma-2-2b-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Access Gemma on Hugging Face

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately.

Gemma Model Card

Model Page: Gemma

This model card corresponds to the 2b pretrained version of the Gemma 2 model in GGUF Format. The weights here are float32.

In llama.cpp, and other related tools such as Ollama and LM Studio, please make sure that you have these flags set correctly, especially repeat-penalty. Georgi Gerganov (llama.cpp's author) shared his experience in https://huggingface.co/google/gemma-7b-it/discussions/38#65d7b14adb51f7c160769fa1.

You can also visit the model card of the 2B instruct v2 model GGUF.

Resources and Technical Documentation:

Terms of Use: Terms

Authors: Google

Model Information

Summary description and brief definition of inputs and outputs.

Description

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.