mudler / LFM2-24B-A2B-APEX-GGUF

huggingface.co
Total runs: 3.7K
24-hour runs: -147
7-day runs: -417
30-day runs: 1.5K
Model's Last Updated: August 17 2026

Introduction of LFM2-24B-A2B-APEX-GGUF

Model Details of LFM2-24B-A2B-APEX-GGUF

LFM2-24B-A2B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of LFM2-24B-A2B by LiquidAI.

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

Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF .

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 : 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
  • Calibration : v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)
Run with LocalAI
local-ai run mudler/[email protected]
Credits

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

Runs of mudler LFM2-24B-A2B-APEX-GGUF on huggingface.co

3.7K
Total runs
-147
24-hour runs
-542
3-day runs
-417
7-day runs
1.5K
30-day runs

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Updated:June 22 2026