README.md

---
license: apache-2.0
base_model: Jackrong/Gemopus-4-26B-A4B-it-Preview
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - gemma4
---





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<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>
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<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>
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# Gemopus 4 26B-A4B APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [Jackrong/Gemopus-4-26B-A4B-it-Preview](https://huggingface.co/Jackrong/Gemopus-4-26B-A4B-it-Preview).

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant)

## Available Files

| File | Profile | Size | Best For |
|------|---------|------|----------|
| gemopus-4-26B-A4B-APEX-I-Quality.gguf | I-Quality | 20 GB | Highest quality with imatrix |
| gemopus-4-26B-A4B-APEX-Quality.gguf | Quality | 20 GB | Highest quality standard |
| gemopus-4-26B-A4B-APEX-I-Balanced.gguf | I-Balanced | 19 GB | Best overall quality/size ratio |
| gemopus-4-26B-A4B-APEX-Balanced.gguf | Balanced | 19 GB | General purpose |
| gemopus-4-26B-A4B-APEX-I-Compact.gguf | I-Compact | 15 GB | Consumer GPUs, best quality/size |
| gemopus-4-26B-A4B-APEX-Compact.gguf | Compact | 15 GB | Consumer GPUs |
| gemopus-4-26B-A4B-APEX-I-Mini.gguf | I-Mini | 13 GB | Smallest viable, fastest inference |
| gemopus-4-26B-A4B-F16.gguf | F16 | 48 GB | Full precision reference |

## Benchmark Results (Native Evals)

| Model | Size | PPL | KL mean | HellaSwag | Winogrande | MMLU | ARC | TruthfulQA | pp512 t/s | tg128 t/s |
|-------|------|-----|---------|-----------|------------|------|-----|------------|-----------|-----------|
| APEX-I-Quality | 19G | 1223.5 | 0.532 | 50.5 | 59.2 | 32.1 | 35.1 | 31.0 | 5632 | 145.9 |
| APEX-Quality | 19G | 1203.1 | 0.579 | 49.0 | 58.5 | 33.7 | 36.8 | 29.3 | 5623 | 143.5 |
| APEX-I-Balanced | 18G | 1216.4 | 0.600 | 50.0 | 57.2 | 32.6 | 33.4 | 29.9 | 6211 | 149.4 |
| APEX-Balanced | 18G | 1117.9 | 0.702 | 47.8 | 57.2 | 33.6 | 34.1 | 31.1 | 6221 | 145.7 |
| APEX-I-Compact | 14G | 1258.5 | 0.943 | 49.0 | 59.0 | 32.6 | 34.1 | 30.1 | 6612 | 146.7 |
| APEX-Compact | 14G | 782.1 | 1.617 | 48.8 | 58.2 | 33.5 | 34.4 | 30.0 | 6517 | 142.2 |
| APEX-I-Mini | 12G | 1915.3 | 1.907 | 52.0 | 58.2 | 34.4 | 33.4 | 30.8 | 5904 | 146.8 |
| F16 (ref) | 48G | 1215.9 | - | - | - | - | - | - | 2718 | 97.9 |

## 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](https://github.com/mudler/apex-quant) for full details.

## Architecture

- **Base Model**: [Jackrong/Gemopus-4-26B-A4B-it-Preview](https://huggingface.co/Jackrong/Gemopus-4-26B-A4B-it-Preview)
- **Architecture**: Gemma 4 26B-A4B (MoE)
- **Layers**: 30
- **Experts**: 128 routed (8 active per token)
- **Total Parameters**: 26B
- **Active Parameters**: ~4B per token
- **APEX Config**: 5+5 symmetric edge gradient across 30 layers
- **Calibration**: v1.2 diverse dataset

## Run with LocalAI

```bash
local-ai run mudler/Gemopus-4-26B-A4B-it-Preview-APEX-GGUF@gemopus-4-26B-A4B-APEX-I-Balanced.gguf
```

## Credits

APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).