---
license: other
base_model: LiquidAI/LFM2-24B-A2B
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - liquidai
  - lfm2
  - hybrid
---





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# LFM2-24B-A2B APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [LFM2-24B-A2B](https://huggingface.co/LiquidAI/LFM2-24B-A2B) by LiquidAI.

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

## Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see [mudler/Qwen3.5-35B-A3B-APEX-GGUF](https://huggingface.co/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](https://github.com/mudler/apex-quant) 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

```bash
local-ai run mudler/LFM2-24B-A2B-APEX-GGUF@LFM2-24B-A2B-APEX-I-Balanced.gguf
```

## Credits

APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).