Instructions to use google/gemma-7b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-7b-it-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="google/gemma-7b-it-GGUF",
	filename="gemma-7b-it.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use google/gemma-7b-it-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-7b-it-GGUF
# Run inference directly in the terminal:
llama-cli -hf google/gemma-7b-it-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf google/gemma-7b-it-GGUF
# Run inference directly in the terminal:
llama-cli -hf google/gemma-7b-it-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-7b-it-GGUF
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-7b-it-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-7b-it-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-7b-it-GGUF

Use Docker

docker model run hf.co/google/gemma-7b-it-GGUF

LM Studio
Jan
Ollama
How to use google/gemma-7b-it-GGUF with Ollama:
```
ollama run hf.co/google/gemma-7b-it-GGUF
```

Unsloth Studio

How to use google/gemma-7b-it-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-7b-it-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-7b-it-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-7b-it-GGUF to start chatting

Docker Model Runner
How to use google/gemma-7b-it-GGUF with Docker Model Runner:
```
docker model run hf.co/google/gemma-7b-it-GGUF
```

Lemonade

How to use google/gemma-7b-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/gemma-7b-it-GGUF

Run and chat with the model

lemonade run user.gemma-7b-it-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Quantized Models?

by PFnove - opened Apr 5, 2024

Discussion

PFnove

Apr 5, 2024

Almost none of the people interested in running a 7B model want or need to run the f32 model (vram isn't infinite on consumer GPUs)
It would be nice to have some models with quantized weights in GGUF format (most people don't want to download a file over 30gb just to quantize it down to 8 or 4 bits)
float32 weights only make sense on the gemma-2b and gemma-7b models (the ones that aren't already instruction-tuned or in GGUF, a format that doesn't allow finetuning)

lkv

Google org Aug 26, 2025

•

edited Aug 26, 2025

Hi @PFnove , Sorry for late response,

You're right , the float32 versions are not the workhorses of the consumer LLM space. They are the foundational checkpoints , which serve as the source material for creating the more accessible, quantized versions. This is why you often see models released in a full-precision format first—it allows the community to create a variety of quantized versions optimized for different hardware and use cases.

The Hugging Face Hub is the central repository for pre-quantized models. You'll find that many community members, particularly those from projects like llama.cpp and creators like TheBloke, have already done the work of converting and quantizing popular models. You can filter the models by the GGUF tag to find a comprehensive list.

Thank you.

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