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

Libraries

How to use sayhan/gemma-7b-GGUF-quantized with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sayhan/gemma-7b-GGUF-quantized")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sayhan/gemma-7b-GGUF-quantized", dtype="auto")

llama-cpp-python

How to use sayhan/gemma-7b-GGUF-quantized with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sayhan/gemma-7b-GGUF-quantized",
	filename="gemma-7b-fp16.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 sayhan/gemma-7b-GGUF-quantized with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sayhan/gemma-7b-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sayhan/gemma-7b-GGUF-quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sayhan/gemma-7b-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sayhan/gemma-7b-GGUF-quantized:Q4_K_M

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 sayhan/gemma-7b-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sayhan/gemma-7b-GGUF-quantized:Q4_K_M

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 sayhan/gemma-7b-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sayhan/gemma-7b-GGUF-quantized:Q4_K_M

Use Docker

docker model run hf.co/sayhan/gemma-7b-GGUF-quantized:Q4_K_M

LM Studio
Jan

vLLM

How to use sayhan/gemma-7b-GGUF-quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayhan/gemma-7b-GGUF-quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayhan/gemma-7b-GGUF-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sayhan/gemma-7b-GGUF-quantized:Q4_K_M

SGLang

How to use sayhan/gemma-7b-GGUF-quantized with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sayhan/gemma-7b-GGUF-quantized" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayhan/gemma-7b-GGUF-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sayhan/gemma-7b-GGUF-quantized" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayhan/gemma-7b-GGUF-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use sayhan/gemma-7b-GGUF-quantized with Ollama:
```
ollama run hf.co/sayhan/gemma-7b-GGUF-quantized:Q4_K_M
```

Unsloth Studio new

How to use sayhan/gemma-7b-GGUF-quantized 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 sayhan/gemma-7b-GGUF-quantized 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 sayhan/gemma-7b-GGUF-quantized to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sayhan/gemma-7b-GGUF-quantized to start chatting

Docker Model Runner
How to use sayhan/gemma-7b-GGUF-quantized with Docker Model Runner:
```
docker model run hf.co/sayhan/gemma-7b-GGUF-quantized:Q4_K_M
```

Lemonade

How to use sayhan/gemma-7b-GGUF-quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sayhan/gemma-7b-GGUF-quantized:Q4_K_M

Run and chat with the model

lemonade run user.gemma-7b-GGUF-quantized-Q4_K_M

List all available models

lemonade list

Google Gemma 7B

Model creator: Google
Original model: gemma-7b-it
Terms of use

Description

This repo contains GGUF format model files for Google's Gemma 7B

Original model

Developed by: Google

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.

Quantizon types

quantization method	bits	size	description	recommended
Q2_K	2	3.09	very small, very high quality loss	❌
Q3_K_S	3	3.68 GB	very small, high quality loss	❌
Q3_K_L	3	4.4 GB	small, substantial quality loss	❌
Q4_0	4	4.81 GB	legacy; small, very high quality loss	❌
Q4_K_S	4	4.84 GB	medium, balanced quality	✅
Q4_K_M	4	5.13 GB	medium, balanced quality	✅
Q5_0	5	5.88 GB	legacy; medium, balanced quality	❌
Q5_K_S	5	5.88 GB	large, low quality loss	✅
Q5_K_M	5	6.04 GB	large, very low quality loss	✅
Q6_K	6	7.01 GB	very large, extremely low quality loss	❌
Q8_0	8	9.08 GB	very large, extremely low quality loss	❌
FP16	16	17.1 GB	enormous, negligible quality loss	❌

Usage

You can use this model with the latest builds of LM Studio and llama.cpp.
If you're new to the world of large language models, I recommend starting with LM Studio.

Downloads last month: 141

GGUF

Model size

9B params

Architecture

gemma

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for sayhan/gemma-7b-GGUF-quantized

Base model

google/gemma-7b

Quantized

(21)

this model