---

license: other

license_name: lfm1.0

base_model:

• LiquidAI/LFM2.5-24B-A2B

tags:

• gguf
• lfm2
• moe
• text-generation

---

LFM2.5-24B-A2B — antirez-style Q4 / Q3 / Q3+ GGUF

GGUF for mainstream llama.cpp (LFM2 MoE support, llama.cpp ≥ b7164).

Recipe (antirez style)

| Tensor group | Q4 file | Q3 file (Q3\_K down) | Q3+ file (Q4\_K down) |

| --- | --- | --- | --- |

| Routed expert gate / up (76 tensors) | IQ4\_XS | IQ3\_XXS | IQ3\_XXS |

| Routed expert down (38 tensors) | Q4\_K | Q3\_K | Q4\_K |

| Shared experts, attention, shortconv projections, output | Q8\_0 | Q8\_0 | Q8\_0 |

| Token embeddings | F16 | F16 | F16 |

| Routers, norms, conv | F32 | F32 | F32 |

Calibrated with bartowski's imatrix (487 chunks, full expert coverage).

Everything except the routed experts is kept at Q8\_0 or better, so only the

64-expert FFN carries the quantization noise. KL-checked against the Q8_0

reference on short prompts (2 chunks @ ctx 60). Note: this model's experts

are more quantization-sensitive than Ling-3.0-tiny's — Q4 lands at the KL

level G9v3's Q2 showed, and Q3 roughly doubles that:

| File | KL divergence | Same top token | PPL (quant vs Q8\_0) |

| --- | --- | --- | --- |

| Q4 | 0.22 / 0.34 | 83% / 74% | 31.0 / 18.1 vs 15.5 |

| Q3 | 0.56 / 0.44 | 66% / 67% | 36.1 / 15.7 vs 15.5 |

| Q3+ (Q4\_K down) | 0.47 / 0.46 | 72% / 66% | 40.0 / 21.0 vs 15.5 |

Files

| File | Size | BPW |

| --- | ---: | ---: |

| LFM2.5-24B-A2B-Q3-IQ3_XXS-Q3_K.gguf | 9.77 GiB | 3.50 |

| LFM2.5-24B-A2B-Q3-IQ3_XXS-Q4_K.gguf | 10.71 GiB | 3.84 |

| LFM2.5-24B-A2B-Q4-IQ4_XS-Q4_K.gguf | 12.84 GiB | 4.60 |

Model

LFM2.5-24B-A2B is a 64-expert MoE with ~2 B active parameters out of 24 B

total (4 experts per token). Hybrid architecture: depthwise shortconv blocks

mixed with GQA attention, 38 MoE layers, hidden size 2048. Context: 131 072

tokens.

Original model: LiquidAI/LFM2.5-24B-A2B.

Imatrix and BF16 source: bartowski/LiquidAI_LFM2.5-24B-A2B-GGUF.

License: LFM Open License v1.0.