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liodon-ai/Qwen2.5-1.5B-Instruct-imatrix-GGUF overview

Qwen2.5 1.5B Instruct — iMatrix GGUF GGUF quantizations of Qwen/Qwen2.5 1.5B Instruct https://huggingface.co/Qwen/Qwen2.5 1.5B Instruct , published by Liodon A…

ggufollamalocal-llmllama.cpplm-studioquantizedimatrixsub-4-bitqwen2base_model:Qwen/Qwen2.5-1.5Btext-generationbase_model:Qwen/Qwen2.5-1.5B-Instructbase_model:quantized:Qwen/Qwen2.5-1.5B-Instructlicense:otherendpoints_compatibleregion:usconversational

Runs locally from ~573.2 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
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Repository Files & Downloads

7 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwen2.5-1.5B-Instruct-IQ2_M.ggufGGUFIQ2_M573.2 MBDownload
Qwen2.5-1.5B-Instruct-IQ3_M.ggufGGUFIQ3_M740.7 MBDownload
Qwen2.5-1.5B-Instruct-IQ4_XS.ggufGGUFIQ4_XS854.2 MBDownload
Qwen2.5-1.5B-Instruct-Q4_K_M.ggufGGUFQ4_K_M940.4 MBDownload
Qwen2.5-1.5B-Instruct-Q5_K_M.ggufGGUFQ5_K_M1.05 GBDownload
Qwen2.5-1.5B-Instruct-Q6_K.ggufGGUFQ6_K1.19 GBDownload
Qwen2.5-1.5B-Instruct-Q8_0.ggufGGUFQ8_01.53 GBDownload

Model Details

Model IDliodon-ai/Qwen2.5-1.5B-Instruct-imatrix-GGUF
Authorliodon-ai
Pipelinetext-generation
Licenseother
Base modelQwen/Qwen2.5-1.5B-Instruct
Last modified2026-07-13T04:56:43.000Z

Model README

---

license: other

base_model: Qwen/Qwen2.5-1.5B-Instruct

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
  • qwen2
  • base_model:Qwen/Qwen2.5-1.5B

quantized_by: liodon-ai

---

Qwen2.5-1.5B-Instruct — iMatrix GGUF

GGUF quantizations of Qwen/Qwen2.5-1.5B-Instruct, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/Qwen2.5-1.5B-Instruct-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/Qwen2.5-1.5B-Instruct-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/Qwen2.5-1.5B-Instruct-imatrix-GGUF and pick your quant.

Quants

| Quant | Size | VRAM est. | Notes |

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

| IQ2_M | 0.60 GB | ~1 GB | 2-bit, iMatrix — smallest usable |

| IQ3_M | 0.78 GB | ~1 GB | 3-bit, iMatrix — great quality/size tradeoff |

| IQ4_XS | 0.90 GB | ~1 GB | 4-bit extra-small, iMatrix |

| Q4_K_M | 0.99 GB | ~1 GB | 4-bit, iMatrix-calibrated (recommended) |

| Q5_K_M | 1.13 GB | ~1 GB | 5-bit, iMatrix-calibrated |

| Q6_K | 1.27 GB | ~1 GB | 6-bit, iMatrix-calibrated, near-lossless |

| Q8_0 | 1.65 GB | ~2 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/Qwen2.5-1.5B-Instruct-GGUF

Source

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Quantized by Liodon AI

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