This repository contains heavily optimized, quantized
GGUF formats of the
Google Gemma 4 26B-A4B Instruction-Tuned model, proudly published and maintained by the engineering team at
CQ Systems.
Our primary mission is the democratization of foundational intelligence. By rigorously compressing and optimizing this frontier-level architecture using custom Importance Matrices (iMatrix) and advanced K-quants, we have engineered a solution that allows researchers, developers, and local AI enthusiasts to run state-of-the-art cognitive engines entirely offline on standard consumer hardware.
⚡ The MoE Paradigm: Understanding the 26B-A4B Architecture
This specific variant of the Gemma 4 family utilizes a highly advanced Mixture of Experts (MoE) architecture, denoted by the 26B-A4B nomenclature. Understanding this structure is crucial for maximizing local deployment performance.
Traditional "dense" models activate every single parameter they possess for every single token generated. A standard 26-billion parameter model would be incredibly slow and resource-heavy.
Gemma 4 26B-A4B fundamentally changes this paradigm. It contains 26 Billion total parameters to store a vast repository of deep knowledge, nuanced logic, and linguistic understanding. However, during inference, a dynamic "router" network evaluates each incoming token and activates only the most relevant expert sub-networks, utilizing just 4 Billion active parameters (A4B) at any given time.
What does this mean for local deployment on Apple Silicon?
- Memory Capacity (RAM): You still need sufficient unified memory to store the entire 26B model state in your hardware's memory banks.
- Compute Speed (Bandwidth/Compute): Because only 4B parameters are actively multiplied during generation, the model achieves the blazing-fast token generation speeds and exceptionally low thermal output of a tiny edge model.
- The Result: It is the perfect equilibrium. You receive the heavyweight reasoning and factual recall of a massive foundation model, combined with the lightning-fast generation speed necessary for real-time, interactive, agentic workflows on Apple Silicon architectures.
📦 Available Files & Hardware Scaling
We provide a comprehensive spectrum of quantized files, allowing you to perfectly align the model's footprint with your available unified memory constraints. All quantized versions (Q4_K and Q8_0) were created using a highly calibrated CQ Systems Importance Matrix (iMatrix) to minimize perplexity loss and preserve complex reasoning capabilities.
| Filename | Size | Quantization | Recommendation & Technical Notes |
|---|
| CQ-Gemma-4-26B-A4B-It-bf16-q4_K.gguf | 16.8 GB | Q4_K | 🏆 Recommended for General Use. This K-quant provides an exceptional balance of reasoning retention and manageable memory footprint. Requires a machine with at least 24GB of total unified memory to run comfortably alongside a moderate context window. |
| CQ-Gemma-4-26B-A4B-It-bf16-q8_0.gguf | 26.9 GB | Q8_0 | High-fidelity 8-bit integer quantization. This delivers near-uncompressed reasoning quality with zero discernible logic degradation, ideal for complex coding or deep logical deduction tasks. Requires 32GB+ of unified memory. |
| CQ-Gemma-4-26B-A4B-It-bf16.gguf | 50.5 GB | BF16 | Baseline uncompressed BFloat16. Highest absolute fidelity. Recommended exclusively for academic research, targeted LoRA fine-tuning, or base weight extraction. Requires 64GB+ RAM. |
| CQ-Gemma-4-26B-A4B-It-f16.gguf | 50.5 GB | F16 | Baseline uncompressed Float16. Alternative high-fidelity baseline for systems optimized for FP16 mathematical operations rather than BF16. Requires 64GB+ RAM. |
🚀 Deployment Instructions
1. Command Line Interface: llama.cpp
You can run this model directly in your terminal using the compiled llama.cpp CLI tool. To ensure maximum performance on Apple devices, ensure you have built llama.cpp with Metal framework support enabled.
Launch an interactive chat session with the recommended Q4_K model
./llama-cli -m CQ-Gemma-4-26B-A4B-It-bf16-q4_K.gguf \
-c 8192 \
-n 1024 \
--n-gpu-layers 99 \
--color \
-p "<|think|>\nAnalyze the system architecture.\n<|channel>thought\n"
- -c 8192: Sets the context window to 8K tokens. (Note: While the model supports up to 128K, expanding the context window will proportionally increase your RAM usage for the KV Cache. Adjust this based on your available headroom).
- -n 1024: Limits the maximum generated response to 1024 tokens.
- --n-gpu-layers 99: Forces the offloading of all model layers to the Metal GPU for maximum acceleration.
2. Graphical Interfaces: LM Studio / Ollama
For users who prefer a streamlined, graphical interface or API server:
- Download: Select and download the .gguf file that best matches your system's unified memory capabilities from the table above.
- LM Studio Integration: Drag and drop the downloaded .gguf file directly into your LM Studio local models directory. In the LM Studio settings, ensure "Hardware Offload" is enabled and set to maximum (Apple Metal). Adjust your context length slider carefully to avoid exceeding your total system memory.
- Ollama Integration: Create a plain text file named Modelfile in the same directory as your download. Add the following line:
FROM ./CQ-Gemma-4-26B-A4B-It-bf16-q4_K.gguf
Then, execute the build command in your terminal: ollama create cq-gemma-26b-moe -f Modelfile.
(Note: While the foundational Gemma 4 family possesses native multimodal capabilities for audio and image processing, standard GGUF text-generation architectures primarily support text inference out-of-the-box. Enabling full multimodal inference in GGUF environments requires compiling an accompanying mmproj visual/audio encoder file).
🧠 Base Model Specifications: Gemma 4 26B-A4B
The Gemma 4 architecture, developed by Google DeepMind, represents the frontier of open-weights intelligence. The 26B-A4B variant specifically targets the "goldilocks zone" between ultra-light edge models (like the E2B) and massive datacenter behemoths (like the 31B Dense model).
- Total Knowledge Parameters: 26 Billion (Mixture of Experts)
- Active Compute Parameters: 4 Billion per forward pass
- Maximum Context Length: 128,000 tokens (Capable of ingesting entire books, codebases, or extended conversational histories in a single prompt).
- Standard System Prompts: Gemma 4 natively utilizes standard system, assistant, and user structural roles.
💭 The Cognitive Engine: Thinking Mode Configuration
One of the most profound advancements in the Gemma 4 architecture is its capability for "Chain of Thought" (CoT) reasoning. Rather than immediately generating an answer, the model can be instructed to internally map out a logical plan, evaluate constraints, and self-correct before outputting a final response.
To activate this profound reasoning capability, you must include the <|think|> token at the very beginning of your system prompt.
When thinking mode is successfully engaged, the model will output its internal reasoning scratchpad, followed strictly by the final, refined answer, utilizing the following architectural formatting:
<|channel>thought\n
[The model's internal, step-by-step reasoning process, evaluating the query, searching for edge cases, and formulating a strategy.]
<channel|>
[The final, polished, and highly accurate answer provided to the user.]
This structured thinking process dramatically reduces hallucinations in complex coding, mathematical, and logical deduction scenarios.
📜 License & Acknowledgements
- Foundational Architecture: Developed by Google DeepMind
- Licensing: Gemma Open Use License
- Pipeline Engineering & Quantization: The CQ Systems Engineering Team
For comprehensive benchmark matrices, thorough evaluations, architectural whitepapers, ethical considerations, and safety deployment metrics, we highly encourage users to refer to the official
Google Gemma 4 Technical Documentation.
