File	Profile	Size	Best For
LFM2.5-8B-A1B-APEX-I-Quality.gguf	I-Quality	6.1 GB	Highest quality with imatrix
LFM2.5-8B-A1B-APEX-Quality.gguf	Quality	6.1 GB	Highest quality standard
LFM2.5-8B-A1B-APEX-I-Balanced.gguf	I-Balanced	6.3 GB	Best overall quality/size ratio
LFM2.5-8B-A1B-APEX-Balanced.gguf	Balanced	6.3 GB	General purpose
LFM2.5-8B-A1B-APEX-I-Compact.gguf	I-Compact	4.2 GB	Consumer GPUs, best quality/size
LFM2.5-8B-A1B-APEX-Compact.gguf	Compact	4.2 GB	Consumer GPUs
LFM2.5-8B-A1B-APEX-I-Mini.gguf	I-Mini	3.6 GB	Smallest viable, fastest inference

(I-variants use imatrix-calibrated quantization; the matching base profiles are the same size without imatrix weighting.)

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention, token-mixing) 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, multilingual, Wikipedia).

For this hybrid architecture, APEX additionally:

Applies the edge gradient to the routed experts (the dominant parameter cost).
Treats the short-convolution token-mixing tensors (shortconv.in_proj/out_proj) like attention — keeping the per-layer attention precision rather than the flat fallback.
Keeps the 2 leading dense FFN layers at edge (shared) precision.

See the APEX project for full details.

Architecture

Base Model: LiquidAI/LFM2.5-8B-A1B
Architecture: lfm2_moe — hybrid short-convolution + attention MoE
Layers: 24 (2 leading dense + 22 MoE)
Layer mix: 18 short-convolution + 6 full-attention layers
Experts: 32 routed (4 active per token)
Total Parameters: ~8B
Active Parameters: ~1B per token

Run with LocalAI

local-ai run mudler/LFM2.5-8B-A1B-APEX-GGUF@LFM2.5-8B-A1B-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.