GraySoft
Projects Models Compare Cloud benchmarks FAQ Download guIDE →
Model Intelligence Sheet

liodon-ai/Qwen3-1.7B-imatrix-GGUF overview

Qwen3 1.7B — iMatrix GGUF GGUF quantizations of Qwen/Qwen3 1.7B https://huggingface.co/Qwen/Qwen3 1.7B , published by Liodon AI https://huggingface.co/liodon a…

ggufollamalocal-llmllama.cpplm-studioquantizedimatrixsub-4-bitqwen3base_model:Qwen/Qwen3-1.7B-Basetext-generationbase_model:Qwen/Qwen3-1.7Bbase_model:quantized:Qwen/Qwen3-1.7Blicense:otherendpoints_compatibleregion:usconversational

Runs locally from ~790.5 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
489
Likes
0
Pipeline
text-generation
Author

Repository Files & Downloads

7 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwen3-1.7B-IQ2_M.ggufGGUFIQ2_M790.5 MBDownload
Qwen3-1.7B-IQ3_M.ggufGGUFIQ3_M981.7 MBDownload
Qwen3-1.7B-IQ4_XS.ggufGGUFIQ4_XS1.09 GBDownload
Qwen3-1.7B-Q4_K_M.ggufGGUFQ4_K_M1.19 GBDownload
Qwen3-1.7B-Q5_K_M.ggufGGUFQ5_K_M1.37 GBDownload
Qwen3-1.7B-Q6_K.ggufGGUFQ6_K1.56 GBDownload
Qwen3-1.7B-Q8_0.ggufGGUFQ8_02.02 GBDownload

Model Details

Model IDliodon-ai/Qwen3-1.7B-imatrix-GGUF
Authorliodon-ai
Pipelinetext-generation
Licenseother
Base modelQwen/Qwen3-1.7B
Last modified2026-07-13T04:55:22.000Z

Model README

---

license: other

base_model: Qwen/Qwen3-1.7B

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
  • qwen3
  • base_model:Qwen/Qwen3-1.7B-Base

quantized_by: liodon-ai

---

Qwen3-1.7B — iMatrix GGUF

GGUF quantizations of Qwen/Qwen3-1.7B, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/Qwen3-1.7B-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/Qwen3-1.7B-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/Qwen3-1.7B-imatrix-GGUF and pick your quant.

Quants

| Quant | Size | VRAM est. | Notes |

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

| IQ2_M | 0.83 GB | ~1 GB | 2-bit, iMatrix — smallest usable |

| IQ3_M | 1.03 GB | ~1 GB | 3-bit, iMatrix — great quality/size tradeoff |

| IQ4_XS | 1.18 GB | ~1 GB | 4-bit extra-small, iMatrix |

| Q4_K_M | 1.28 GB | ~1 GB | 4-bit, iMatrix-calibrated (recommended) |

| Q5_K_M | 1.47 GB | ~2 GB | 5-bit, iMatrix-calibrated |

| Q6_K | 1.67 GB | ~2 GB | 6-bit, iMatrix-calibrated, near-lossless |

| Q8_0 | 2.17 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/Qwen3-1.7B-GGUF

Source

---

Quantized by Liodon AI

Run liodon-ai/Qwen3-1.7B-imatrix-GGUF with guIDE

Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.

Download guIDE → · Browse 524k+ models · Compare models

Source: Hugging Face · Compare models