mudler/Qwopus-MoE-35B-A3B-APEX-GGUF

Qwopus-MoE-35B-A3B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Qwopus-MoE-35B-A3B.

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

Available Files

File	Profile	Size	Best For
Qwopus-MoE-35B-A3B-APEX-I-Balanced.gguf	I-Balanced	TBD	Best overall quality/size ratio (with imatrix)
Qwopus-MoE-35B-A3B-APEX-I-Quality.gguf	I-Quality	TBD	Best quality/compression ratio (with imatrix)
Qwopus-MoE-35B-A3B-APEX-Quality.gguf	Quality	TBD	Best quality/compression ratio
Qwopus-MoE-35B-A3B-APEX-Balanced.gguf	Balanced	TBD	Best absolute quality
Qwopus-MoE-35B-A3B-APEX-I-Compact.gguf	I-Compact	TBD	Consumer GPUs (with imatrix)
Qwopus-MoE-35B-A3B-APEX-Compact.gguf	Compact	TBD	Consumer GPUs
Qwopus-MoE-35B-A3B-APEX-I-Mini.gguf	I-Mini	TBD	Smallest viable

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: Qwopus-MoE-35B-A3B (qwen3_5_moe)
• Layers: 40 (hybrid: linear attention + full attention every 4th layer)
• Experts: 256 routed (8 active per token)
• Total Parameters: ~35B
• Active Parameters: ~3B per token
• Origin: Claude Opus 4.6 QLoRA distill of Qwen3.5-35B-A3B
• APEX Config: 5+5 symmetric edge gradient across 40 layers
• Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)
• Source: samuelcardillo/Qwopus-MoE-35B-A3B

Run with LocalAI

local-ai run mudler/Qwopus-MoE-35B-A3B-APEX-GGUF@Qwopus-MoE-35B-A3B-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.