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
: LFM2-24B-A2B (lfm2_moe) by LiquidAI
Layers
: 40 (30 convolutional + 10 full attention, hybrid)
Experts
: 64 routed (4 active per token) + 2 dense layers
Total Parameters
: 24B
Active Parameters
: ~2B per token
APEX Config
: 5+5 symmetric edge gradient across 40 layers
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