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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/gemma-7b-GGUF",
	filename="gemma-7b_Q4_k_m.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 SandLogicTechnologies/gemma-7b-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio new

How to use SandLogicTechnologies/gemma-7b-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 SandLogicTechnologies/gemma-7b-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 SandLogicTechnologies/gemma-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Quantized Gemma-7B (GGUF)

Gemma-7B is a 7-billion-parameter open-weight large language model released by Google, designed for high-quality text generation, reasoning, and instruction-following tasks. This repository provides GGUF-quantized versions of Gemma-7B, optimized for efficient local and edge deployment.

The available Q4_K_M and Q5_K_M variants significantly reduce memory usage and improve inference speed on CPUs and consumer GPUs, while maintaining strong language understanding and response quality.

Model Overview

Model Name: Gemma-7B
Base Model: google/gemma-7b
Architecture: Decoder-only Transformer
Quantized Variants:
- Q4_K_M (4-bit)
- Q5_K_M (5-bit)
Parameter Count: 7 Billion
Context Length: 8K tokens
Modalities: Text
Developer: Google
License: Gemma License

Quantization Formats

Q4_K_M

Approx. ~~70% size reduction (~~4.96 GB)
Aggressive compression for low-memory systems
Significantly reduced model size
Faster inference on CPUs
Best suited for lightweight or edge deployments

Q5_K_M

Approx. ~~66% size reduction (~~5.72 GB)
Higher precision than Q4
Improved coherence and reasoning stability
Recommended for balanced performance and quality

Training Background (Original Model)

Gemma-7B is trained using the same research principles that underpin Google’s larger Gemini models, with a focus on safety, performance, and transparency.

Pretraining

Trained on a diverse mixture of licensed data, data created by human trainers, and publicly available text
Optimized using autoregressive language modeling
Strong emphasis on general reasoning, instruction comprehension, and fluent English generation

Alignment & Instruction Tuning

Further tuned to improve helpfulness and instruction adherence
Designed to generate clear, structured, and relevant responses
Emphasizes responsible and safe language generation

Key Capabilities

Instruction-following
Responds accurately to structured prompts and user instructions.
Reasoning and analysis
Handles logical problems, step-by-step explanations, and analytical tasks.
General text generation
Produces fluent, coherent, and context-aware natural language output.
Multi-turn conversations
Maintains conversational context across multiple interactions.
Efficient local inference
GGUF format enables fast execution using llama.cpp and compatible runtimes.

Usage Example

llama.cpp

./llama-cli \
  -m SandLogicTechnologies\gemma-7b_Q4_K_M.gguf \
  -p "Explain large language models in simple terms."

Recommended Applications

Offline AI assistants Run Gemma locally without relying on cloud APIs.
Research and experimentation Ideal for testing prompting strategies, reasoning tasks, and model behavior.
Edge and CPU-based deployment Suitable for laptops, workstations, and low-VRAM environments.
Privacy-focused workflows Keep all inference and data fully local.

Acknowledgments

These quantized models are derived from the original Gemma-7B model released by Google.

Special thanks to:

The Google Gemma team for making high-quality open-weight models available
Georgi Gerganov and the llama.cpp community for GGUF support and efficient inference tooling

Contact

For questions, feedback, or support, please reach out atsupport@sandlogic.com or visit https://www.sandlogic.com/

Downloads last month: 12

GGUF

Model size

9B params

Architecture

gemma

Hardware compatibility

4-bit

5-bit

Model tree for SandLogicTechnologies/gemma-7b-GGUF

Base model

google/gemma-7b

Quantized

(21)

this model