Gemma 3 270M Instruction-Tuned - Q4_K_M Quantized (GGUF)

Model Description

This is a quantized version of Google's Gemma 3 270M instruction-tuned model, optimized for efficient inference on consumer hardware and mobile applications. The model has been converted to GGUF format and quantized using Q4_K_M quantization through llama.cpp, making it perfect for resource-constrained environments.

Model Details

Base Model: google/gemma-3-270m-it
Model Type: Large Language Model (LLM)
Quantization: Q4_K_M
Format: GGUF
File Size: 253MB
Precision: 4-bit quantized weights with mixed precision
Framework: Compatible with llama.cpp, Ollama, and other GGUF-compatible inference engines

Quantization Details

Method: Q4_K_M quantization via llama.cpp
Benefits: Significantly reduced memory footprint while maintaining model quality
Use Case: Optimized for edge deployment, mobile applications, and resource-constrained environments
Performance: Maintains competitive performance compared to the original Gemma 3 instruction-tuned model

Real-World Application

This model is actively used in a production mobile application available on app stores. The app demonstrates the practical viability of running quantized LLMs on mobile devices while maintaining user privacy through on-device inference. The implementation showcases:

On-device AI: No data sent to external servers
Fast inference: Optimized for mobile hardware
Efficient memory usage: Runs smoothly on consumer devices
App Store compliance: Meets all platform requirements including Gemma licensing terms

Usage

With llama.cpp

# Download the model
wget https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf/resolve/main/gemma-270m-q4-k-m.gguf

# Run inference
./main -m gemma-270m-q4-k-m.gguf -p "Your prompt here"

With Ollama

# Create Modelfile
echo "FROM ./gemma-270m-q4-k-m.gguf" > Modelfile

# Create and run
ollama create gemma-270m-q4 -f Modelfile
ollama run gemma-270m-q4

With Python (llama-cpp-python)

from llama_cpp import Llama

# Load model
llm = Llama(model_path="gemma-270m-q4-k-m.gguf")

# Generate text
output = llm("Your prompt here", max_tokens=100)
print(output['choices'][0]['text'])

Mobile Integration

For mobile app development, this model can be integrated using:

iOS: llama.cpp with Swift bindings
Android: JNI wrappers or TensorFlow Lite conversion
React Native: Native modules with llama.cpp
Flutter: Platform channels with native implementations

System Requirements

RAM: Minimum 1GB, Recommended 2GB+
Storage: 300MB for model file
CPU: Modern x86_64 or ARM64 processor
Mobile: iOS 12+ / Android API 21+
OS: Windows, macOS, Linux

Performance Metrics

Metric	Original F16	Q4_K_M	Improvement
Size	~540MB	253MB	53% reduction
RAM Usage	~1GB	~400MB	60% reduction
Inference Speed	Baseline	~2x faster	2x speedup
Mobile Performance	Too large	Excellent	✅ Mobile ready

Performance tested on various devices including mobile hardware

License and Usage

Important: This model is a derivative of Google's Gemma and is subject to the original licensing terms.

Gemma is provided under and subject to the Gemma Terms of Use.

Key Points:

✅ Commercial use permitted under the Gemma license
✅ Mobile app deployment allowed with proper attribution
⚠️ Must comply with the Gemma Prohibited Use Policy
📄 App store compliance: Licensing terms disclosed in app store listings
🔄 Redistribution: Must include proper attribution and license terms

Usage Restrictions

As per the Gemma Terms of Use, this model cannot be used for:

Illegal activities
Child safety violations
Generation of hateful, harassing, or violent content
Generation of false or misleading information
Privacy violations

See the full Prohibited Use Policy for complete details.

Mobile App Compliance

This model is used in compliance with:

Gemma Terms of Use: Full licensing terms disclosed
App Store Guidelines: Platform requirements met
Privacy Standards: On-device processing, no data collection
Performance Standards: Optimized for mobile hardware

Limitations

Quantization may result in slight quality degradation compared to the original Gemma 3 instruction-tuned model
Performance characteristics may vary across different hardware platforms
Subject to the same content limitations as the base Gemma 3 instruction-tuned model
Context length and capabilities inherited from base Gemma 3 270M instruction-tuned model
Mobile performance depends on device specifications

Technical Specifications

Original Parameters: 270M
Quantization Scheme: Q4_K_M (4-bit weights, mixed precision for critical layers)
Context Length: 32,768 tokens (inherited from Gemma 3 270M)
Vocabulary Size: 256,000 tokens
Architecture: Transformer decoder
Attention Heads: 8
Hidden Layers: 18

Download Options

Direct Download

# Using wget
wget https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf/resolve/main/gemma-270m-q4-k-m.gguf

# Using curl
curl -L -o gemma-270m-q4-k-m.gguf https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf/resolve/main/gemma-270m-q4-k-m.gguf

Programmatic Download

# Using huggingface-hub
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Durlabh/gemma-270m-q4-k-m-gguf",
    filename="gemma-270m-q4-k-m.gguf"
)

Citation

If you use this model, please cite both the original Gemma work and acknowledge the quantization:

@misc{durlabh-gemma-270m-q4-k-m,
  title={Gemma 3 270M Instruction-Tuned Q4_K_M Quantized},
  author={Durlabh},
  year={2025},
  note={Quantized version of Google's Gemma 3 270M instruction-tuned model using llama.cpp Q4_K_M},
  url={https://huggingface.co/Durlabh/gemma-270m-q4-k-m-gguf}
}

Original Gemma 3 paper:

@misc{gemma3_2025,
  title={Gemma 3: Google's new open model based on Gemini 2.0},
  author={Gemma Team},
  year={2025},
  publisher={Google},
  url={https://blog.google/technology/developers/gemma-3/}
}

Community & Support

Issues: Report problems or questions in the repository discussions
Mobile Development: See model usage in production mobile applications
Quantization: Built with llama.cpp for optimal performance

Acknowledgments

Google DeepMind team for the original Gemma model
llama.cpp community for the quantization tools and GGUF format
Hugging Face for hosting infrastructure
Georgi Gerganov for creating and maintaining llama.cpp
Mobile AI community for advancing on-device inference

Disclaimer

This is an unofficial quantized version of Gemma 3 created for practical mobile deployment. For official Gemma models, please visit Google's official Gemma page.

The mobile application using this model fully complies with platform guidelines and Gemma licensing requirements.

Ready for production use! This model powers real-world mobile applications while maintaining full compliance with licensing terms.

Durlabh
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gemma-270m-q4-k-m-gguf