SandLogicTechnologies/gemma-7b-GGUF

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.

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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

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Contact

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