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

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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](https://github.com/mudler/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

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.