🚀 Official release: Gemma3 Smart Q4 - Bilingual Offline Assistant for Raspberry Pi

- Bilingual IT/EN support
- Optimized for Raspberry Pi 4/5
- Fully offline inference
- Benchmark: 3.56-4.2 tokens/s
- Two quantizations: Q4_K_M (quality) and Q4_0 (speed)

.gitattributes

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+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: gemma
+language:
+- en
+- it
+tags:
+- gemma
+- gemma3
+- quantized
+- gguf
+- raspberry-pi
+- edge-ai
+- bilingual
+- ollama
+- offline
+model_type: text-generation
+inference: false
+---
+
+# 🧠 Gemma3 Smart Q4 — Bilingual Offline Assistant for Raspberry Pi
+
+**Gemma3 Smart Q4** is a quantized bilingual (Italian–English) variant of Google's Gemma 3 1B model, optimized for edge devices like the **Raspberry Pi 4 & 5**. It runs **completely offline** with Ollama or llama.cpp, ensuring **privacy and speed** without external dependencies.
+
+---
+
+## 💻 Optimized for Raspberry Pi
+
+> ✅ **Tested on Raspberry Pi 4 (4GB)** — average speed 3.56-3.67 tokens/s
+> ✅ **Fully offline** — no external APIs, no internet required
+> ✅ **Lightweight** — under 800 MB in Q4 quantization
+> ✅ **Bilingual** — seamlessly switches between Italian and English
+
+---
+
+## 🔍 Key Features
+
+- 🗣️ **Bilingual AI** — Automatically detects and responds in Italian or English
+- ⚡ **Edge-optimized** — Fine-tuned parameters for low-power ARM devices
+- 🔒 **Privacy-first** — All inference happens locally on your device
+- 🧩 **Two quantizations available**:
+  - **Q4_K_M** (≈769 MB) → Better quality, more coherent reasoning
+  - **Q4_0** (≈687 MB) → 15-20% faster, ideal for real-time interactions
+
+---
+
+## 📊 Benchmark Results
+
+Tested on **Raspberry Pi 4 (4GB RAM)** with Ollama:
+
+| Model | Avg Speed | Individual Results | File Size | Use Case |
+|-------|-----------|-------------------|-----------|----------|
+| **gemma3-1b-q4_k_m.gguf** | **3.56 tokens/s** | 3.71, 3.58, 3.40 t/s | 769 MB | Better quality, long conversations |
+| **gemma3-1b-q4_0.gguf** | **3.67 tokens/s** | 3.65, 3.67, 3.70 t/s | 687 MB | **Default choice**, general use |
+
+**Test details**:
+- Hardware: Raspberry Pi 4 (4GB RAM)
+- OS: Raspberry Pi OS (Debian Bookworm)
+- Runtime: Ollama 0.x
+- Prompts: Mixed Italian/English, typical assistant queries
+
+> **Recommendation**: Use **Q4_0** as default (3% faster, 82MB smaller, same quality). Use **Q4_K_M** only if you need slightly better coherence in very long conversations (1000+ tokens).
+
+---
+
+## 🛠️ Quick Start with Ollama
+
+### Option 1: Pull from Hugging Face
+
+Create a `Modelfile`:
+
+```bash
+cat > Modelfile <<'MODELFILE'
+FROM hf.co/antonio/gemma3-smart-q4/gemma3-1b-q4_0.gguf
+
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER num_ctx 1024
+PARAMETER num_thread 4
+PARAMETER num_batch 32
+PARAMETER repeat_penalty 1.05
+
+SYSTEM """
+You are an offline AI assistant running on a Raspberry Pi. Automatically detect the user's language (Italian or English) and respond in the same language. Be concise, practical, and helpful. If a task requires internet access or external services, clearly state this and suggest local alternatives when possible.
+
+Sei un assistente AI offline che opera su Raspberry Pi. Rileva automaticamente la lingua dell'utente (italiano o inglese) e rispondi nella stessa lingua. Sii conciso, pratico e utile. Se un compito richiede accesso a internet o servizi esterni, indicalo chiaramente e suggerisci alternative locali quando possibile.
+"""
+MODELFILE
+```
+
+Then run:
+
+```bash
+ollama create gemma3-smart-q4 -f Modelfile
+ollama run gemma3-smart-q4 "Ciao! Chi sei?"
+```
+
+### Option 2: Download and Use Locally
+
+```bash
+# Download the model
+wget https://huggingface.co/antonio/gemma3-smart-q4/resolve/main/gemma3-1b-q4_0.gguf
+
+# Create Modelfile
+cat > Modelfile <<'MODELFILE'
+FROM ./gemma3-1b-q4_0.gguf
+
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER num_ctx 1024
+PARAMETER num_thread 4
+PARAMETER num_batch 32
+PARAMETER repeat_penalty 1.05
+
+SYSTEM """
+You are an offline AI assistant running on a Raspberry Pi. Automatically detect the user's language (Italian or English) and respond in the same language. Be concise, practical, and helpful.
+
+Sei un assistente AI offline su Raspberry Pi. Rileva la lingua dell'utente (italiano o inglese) e rispondi nella stessa lingua. Sii conciso, pratico e utile.
+"""
+MODELFILE
+
+# Create and run
+ollama create gemma3-smart-q4 -f Modelfile
+ollama run gemma3-smart-q4 "Hello! Introduce yourself."
+```
+
+---
+
+## ⚙️ Recommended Parameters
+
+For **Raspberry Pi 4/5**, use these optimized settings:
+
+```yaml
+Temperature: 0.7          # Balanced creativity vs consistency
+Top-p: 0.9                # Nucleus sampling for diverse responses
+Context Length: 1024      # Optimal for Pi 4 memory
+Threads: 4                # Utilizes all Pi 4 cores
+Batch Size: 32            # Optimized for throughput
+Repeat Penalty: 1.05      # Reduces repetitive outputs
+```
+
+For **faster responses** (e.g., voice assistant), reduce `num_ctx` to `512`.
+
+---
+
+## 📦 Files Included
+
+- `gemma3-1b-q4_k_m.gguf` — Q4_K_M quantization (~769 MB) - **Better quality**
+- `gemma3-1b-q4_0.gguf` — Q4_0 quantization (~687 MB) - **Faster speed**
+
+---
+
+## 🔖 License & Attribution
+
+This is a derivative work of **Google's Gemma 3 1B**.
+Please review and comply with the [Gemma License](https://ai.google.dev/gemma/terms).
+
+**Quantization, optimization, and bilingual configuration by Antonio.**
+
+---
+
+## 🔗 Links
+
+- **GitHub Repository**: [antonio/gemma3-smart-q4](https://github.com/antonio/gemma3-smart-q4) — Code, demos, benchmark scripts
+- **Original Model**: [Google Gemma 3 1B IT](https://huggingface.co/google/gemma-3-1b-it)
+- **Ollama Library**: Coming soon (pending submission)
+
+---
+
+## 🚀 Use Cases
+
+- **Privacy-focused personal assistant** — All data stays on your device
+- **Offline home automation** — Control IoT devices without cloud dependencies
+- **Educational projects** — Learn AI/ML without expensive hardware
+- **Voice assistants** — Fast enough for real-time speech interaction
+- **Embedded systems** — Industrial applications requiring offline inference
+
+---
+
+**Built with ❤️ by Antonio 🇮🇹**
+*Empowering privacy and edge computing, one model at a time.*
+

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