GraySoft
Projects Models Compare Cloud benchmarks FAQ Download guIDE →
Model Intelligence Sheet

mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF overview

< apex banner v2 <div style="background color: f59e0b; color: white; padding: 20px; border radius: 10px; text align: center; margin: 20px 0;" <h2 style="color:…

ggufquantizedapexapex-mtpmoemixture-of-expertsqwen3speculative-decodingself-speculativemtpvlmvisioncoderbase_model:Jackrong/Qwopus3.6-35B-A3B-Coderbase_model:quantized:Jackrong/Qwopus3.6-35B-A3B-Coderlicense:apache-2.0region:us

Runs locally from ~861.0 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
0
Likes
3
Pipeline
Author

Repository Files & Downloads

8 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwopus3.6-35B-A3B-Coder-APEX-MTP-Balanced.ggufGGUFGGUF24.27 GBDownload
Qwopus3.6-35B-A3B-Coder-APEX-MTP-Compact.ggufGGUFGGUF16.14 GBDownload
Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Balanced.ggufGGUFGGUF24.27 GBDownload
Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Compact.ggufGGUFGGUF16.14 GBDownload
Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Mini.ggufGGUFGGUF13.29 GBDownload
Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Quality.ggufGGUFGGUF21.87 GBDownload
Qwopus3.6-35B-A3B-Coder-APEX-MTP-Quality.ggufGGUFGGUF21.87 GBDownload
mmproj.ggufGGUFGGUF861.0 MBDownload

Model Details

Model IDmudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF
Authormudler
Pipeline
Licenseapache-2.0
Base modelJackrong/Qwopus3.6-35B-A3B-Coder
Last modified2026-07-02T17:36:52.000Z

Model README

---

license: apache-2.0

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

tags:

- gguf

- quantized

- apex

- apex-mtp

- moe

- mixture-of-experts

- qwen3

- speculative-decoding

- self-speculative

- mtp

- vlm

- vision

- coder

---

<!-- 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-MTP GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Jackrong/Qwopus3.6-35B-A3B-Coder, with the model's MTP (multi-token prediction) head bundled for in-the-box self-speculative decoding.

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

What's different from the plain APEX repo?

This model ships a real MTP head, and these GGUFs bundle it alongside the trunk in a single file (via llama.cpp PR #22673). With a recent llama.cpp you can enable self-speculative decoding from just this one file — no separate draft model:

llama-server -m Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Balanced.gguf --draft-mtp

The non-MTP version is at mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF — slightly smaller, no self-spec.

MTP draft head precision

The bundled MTP head (blk.40. including nextn.) is quantized to Q8_0 (near-lossless) on every tier, keeping draft accuracy high for a good spec-decode acceptance rate at a modest size cost. The MTP head is not imatrix-calibrated (imatrix forward passes only activate the trunk), so it uses static Q8_0.

Available Files

| File | Profile | Best For |

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

| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Balanced.gguf | I-Balanced | Best overall + self-spec |

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

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

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

| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Compact.gguf | I-Compact | Consumer GPUs + self-spec |

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

| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Mini.gguf | I-Mini | Smallest viable + self-spec |

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

Architecture

  • Base: Qwopus3.6-35B-A3B-Coder (Qwen3_5MoeForConditionalGeneration, Qwen3.6-35B-A3B)
  • Layers: 40 trunk + 1 MTP (bundled) · Experts: 256 routed + 1 shared (8 active)
  • Vision: Built-in vision encoder (mmproj included)
  • Calibration: v1.3 diverse dataset

Credits

APEX by the LocalAI team. MTP support: llama.cpp PR #22673. Built on llama.cpp. Base model by Jackrong.

Run mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF with guIDE

Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.

Download guIDE → · Browse 524k+ models · Compare models

Source: Hugging Face · Compare models