---

license: other

license_name: lfm1.0

license_link: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE

base_model: LiquidAI/LFM2.5-8B-A1B

tags:

- gguf

- quantized

- apex

- moe

- mixture-of-experts

- lfm2

---

LFM2.5-8B-A1B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of LiquidAI/LFM2.5-8B-A1B.

Brought to you by the LocalAI team | APEX Project

Available Files

| 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.