license, base_model, base_model_relation, pipeline_tag, library_name, tags, quantized_by

license	base_model	base_model_relation	pipeline_tag	library_name	tags	quantized_by
other	Qwen/Qwen2.5-Coder-7B-Instruct	quantized	text-generation	gguf	gguf ollama local-llm llama.cpp lm-studio quantized imatrix sub-4-bit qwen2 codeqwen	liodon-ai

Qwen2.5-Coder-7B-Instruct — iMatrix GGUF

GGUF quantizations of Qwen/Qwen2.5-Coder-7B-Instruct, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/Qwen2.5-Coder-7B-Instruct-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/Qwen2.5-Coder-7B-Instruct-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/Qwen2.5-Coder-7B-Instruct-imatrix-GGUF and pick your quant.

Quants

Quant	Size	VRAM est.	Notes
IQ2_M	2.78 GB	~3 GB	2-bit, iMatrix — smallest usable
IQ3_M	3.57 GB	~4 GB	3-bit, iMatrix — great quality/size tradeoff
IQ4_XS	4.22 GB	~5 GB	4-bit extra-small, iMatrix
Q4_K_M	4.68 GB	~5 GB	4-bit, iMatrix-calibrated (recommended)
Q5_K_M	5.44 GB	~6 GB	5-bit, iMatrix-calibrated
Q6_K	6.25 GB	~7 GB	6-bit, iMatrix-calibrated, near-lossless
Q8_0	8.10 GB	~9 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-Coder-7B-Instruct-GGUF

Source

• Model: Qwen/Qwen2.5-Coder-7B-Instruct
• License: other

---

Quantized by Liodon AI