--- license: apache-2.0 base_model: google/gemma-4-26B-A4B-it language: - en - ru - de - fr - it - zh - ja - ko - ar pipeline_tag: text-generation tags: - gemma - gemma-4 - gguf - imatrix - quantization - moe - llama-cpp --- # 🧠 Gemma 4 (26B-A4B) MoE - Imatrix Quantized (IQ4_XS) This is a high-performance, hybrid **Mixture of Experts (MoE)** GGUF version of Google's **Gemma 4 26B**. By utilizing a custom **Importance Matrix (imatrix)**, this `IQ4_XS` quantization maintains extreme logical precision while enabling blazing fast inference speeds. In this MoE architecture, only a fraction of parameters are activated per token, making it significantly faster than dense models of similar size. Optimized and compiled by **Krasnopjorovs** ([Artjoms](https://artjoms.ai)). ## 🌍 Multilingual Capabilities The model retains the full 262,144 token vocabulary, showing exceptional reasoning in English, Russian, German, and other major languages. ## 📊 Model Specifications * **Base Model:** Google Gemma 4 26B-A4B (MoE) * **Quantization Format:** IQ4_XS (GGUF) * **Optimization:** Custom Imatrix applied * **File Size:** ~13.92 GB (Extremely VRAM efficient) * **Context Size:** 131,072 tokens * **License:** Gemma ### 🧠 Expert Logic & Coding Tests: The model was subjected to rigorous reasoning and programming benchmarks: 1. **Coding (Autograd from scratch):** Successfully wrote a complete `Value` class for automatic differentiation in Python (Scalar Autograd) with topological sort and backpropagation logic. 2. **Physical Reasoning:** Correctly predicted the gravitational outcome of an overturned table with an open water bottle (zero hallucinations). 3. **Lateral Thinking:** Solved the "Snail on the Wall" and "Family Tree" riddles with 100% accuracy. ## 🛠️ Usage with `llama.cpp` **CLI Example:** ```bash ./llama-cli -m gemma-4-26b-a4b-it-Imatrix-IQ4_XS.gguf -c 131072 -ngl 99 -fa -cnv 🤝 About the Builder Compiled for secure, high-speed local AI ecosystems. For professional AI workstations and private LLM servers, visit artjoms.ai.