mudler/gemma-4-26B-A4B-it-APEX-GGUF
<!-- apex-banner-v2 --> <div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;"> <h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2> <p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>25+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory), enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p> <p style="font-size: 20px; margin: 0;"> <a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> | <a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> | <a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a> </p> <p style="font-size: 14px; margin: 10px 0 0 0; opacity: 0.9;">💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.</p> </div>
Gemma 4 26B-A4B APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of google/gemma-4-26B-A4B-it.
Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team | APEX Project | Technical Report
Benchmark Results
Benchmarks coming soon (re-quantized with llama.cpp b8664 including Gemma 4 tokenizer and logit softcapping fixes). For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.
Available Files
What is APEX?
APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
See the APEX project for full details, technical report, and scripts.
Architecture
- Model: Gemma 4 26B-A4B (google/gemma-4-26B-A4B-it)
- Layers: 30
- Experts: 128 routed (8 active per token)
- Total Parameters: 26B
- Active Parameters: ~4B per token
- Vision: Built-in vision encoder (mmproj included)
- APEX Config: 5+5 symmetric edge gradient across 30 layers
- Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)
- llama.cpp: Built with b8664 (includes Gemma 4 tokenizer fix, logit softcapping, newline split)
Run with LocalAI
local-ai run mudler/gemma-4-26B-A4B-it-APEX-GGUF@gemma-4-26B-A4B-APEX-I-Balanced.ggufCredits
APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.