Qwen2.5-Math-1.5B-Instruct โ€” iMatrix GGUF

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

Quick Start

llama.cpp

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

Ollama

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

LM Studio / Jan โ€” search liodon-ai/Qwen2.5-Math-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 โ€” smallest usable
IQ3_M 0.78 GB ~1 GB 3-bit, iMatrix โ€” 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 โ€” same file size, better output.

Calibration: 2M tokens of WikiText-103.

Also see plain (non-iMatrix) quants: liodon-ai/Qwen2.5-Math-1.5B-Instruct-GGUF

Source


Quantized by Liodon AI

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