---

license: apache-2.0

base_model: Jackrong/Qwopus3.6-35B-A3B-Coder

tags:

- gguf

- quantized

- apex

- moe

- mixture-of-experts

- qwen3

- vlm

- vision

- coder

- code

---

<!-- 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>30+ 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> &nbsp;|&nbsp;

<a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> &nbsp;|&nbsp;

<a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a>

</p>

</div>

Qwopus3.6-35B-A3B-Coder — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Jackrong/Qwopus3.6-35B-A3B-Coder — a Qwen3.6-35B-A3B MoE tuned for coding.

Brought to you by the LocalAI team | APEX Project | Technical Report

> This model ships an MTP head — for self-speculative decoding out of the box, see the MTP-bundled repo: mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF.

Available Files

| File | Profile | Best For |

|------|---------|----------|

| Qwopus3.6-35B-A3B-Coder-APEX-I-Balanced.gguf | I-Balanced | Best overall — imatrix-enhanced |

| Qwopus3.6-35B-A3B-Coder-APEX-I-Quality.gguf | I-Quality | Highest quality with imatrix |

| Qwopus3.6-35B-A3B-Coder-APEX-Quality.gguf | Quality | Highest quality (no imatrix) |

| Qwopus3.6-35B-A3B-Coder-APEX-Balanced.gguf | Balanced | General purpose |

| Qwopus3.6-35B-A3B-Coder-APEX-I-Compact.gguf | I-Compact | Consumer GPUs, imatrix-enhanced |

| Qwopus3.6-35B-A3B-Coder-APEX-Compact.gguf | Compact | Consumer GPUs |

| Qwopus3.6-35B-A3B-Coder-APEX-I-Mini.gguf | I-Mini | Smallest viable, fastest inference |

| mmproj.gguf | Vision projector | Required for image understanding |

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 (first/last 5) 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.

Architecture

• Model: Qwopus3.6-35B-A3B-Coder (Qwen3_5MoeForConditionalGeneration, Qwen3.6-35B-A3B base)
• Layers: 40 · Experts: 256 routed + 1 shared (8 active) · Total/Active: ~35B / ~3B
• Attention: Hybrid (full attention every 4th layer, linear otherwise)
• Vision: Built-in vision encoder (mmproj included)
• Calibration: v1.3 diverse dataset

Run with LocalAI

local-ai run mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF@Qwopus3.6-35B-A3B-Coder-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Built on llama.cpp. Base model by Jackrong.