---

license: other

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

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• ollama
• local-llm
• llama.cpp
• lm-studio
• quantized
• imatrix
• sub-4-bit

quantized_by: liodon-ai

---

LFM2.5-8B-A1B — iMatrix GGUF

GGUF quantizations of LiquidAI/LFM2.5-8B-A1B, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/LFM2.5-8B-A1B-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/LFM2.5-8B-A1B-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/LFM2.5-8B-A1B-imatrix-GGUF and pick your quant.

Quants

| Quant | Size | VRAM est. | Notes |

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

| IQ2_M | 2.84 GB | ~3 GB | 2-bit, iMatrix — smallest usable |

| IQ3_M | 3.78 GB | ~4 GB | 3-bit, iMatrix — great quality/size tradeoff |

| IQ4_XS | 4.59 GB | ~5 GB | 4-bit extra-small, iMatrix |

| Q4_K_M | 5.16 GB | ~6 GB | 4-bit, iMatrix-calibrated (recommended) |

| Q5_K_M | 6.03 GB | ~7 GB | 5-bit, iMatrix-calibrated |

| Q6_K | 6.96 GB | ~8 GB | 6-bit, iMatrix-calibrated, near-lossless |

| Q8_0 | 9.01 GB | ~10 GB | 8-bit, essentially lossless |

What is iMatrix?

Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through

the full-precision model to find which weights matter most, then allocates more precision where

it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following —

same file size, better output.

Calibration: 2M tokens of WikiText-103.

> Also see plain (non-iMatrix) quants: liodon-ai/LFM2.5-8B-A1B-GGUF

Source

• Model: LiquidAI/LFM2.5-8B-A1B
• License: other

---

Quantized by Liodon AI