---
license: gemma
base_model: google/gemma-3-270m-it
tags:
- quantized
- gguf
- llama.cpp
- gemma
- text-generation
- q4_k_m
- edge-deployment
- mobile-app
library_name: gguf
pipeline_tag: text-generation
language:
- en
model_type: gemma
---

# 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](https://huggingface.co/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
```bash
# 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
```bash
# 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)
```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](https://ai.google.dev/gemma/terms).**

### Key Points:
- ✅ **Commercial use permitted** under the Gemma license
- ✅ **Mobile app deployment allowed** with proper attribution
- ⚠️ **Must comply** with the [Gemma Prohibited Use Policy](https://ai.google.dev/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](https://ai.google.dev/gemma/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
```bash
# 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
```python
# 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:

```bibtex
@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:
```bibtex
@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](https://ai.google.dev/gemma).

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.