Qwen3-0.6B-GGUF

NANI-Nithin/Qwen3-0.6B-GGUF

Qwen3-0.6B-GGUF is a text generation model from NANI-Nithin. Use it when you need the model to write or continue text. It is set up for gguf.

GGUF quantizations of Qwen/Qwen3-0.6B, covering 30 files (12.8 GB total).

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.gguf13.8 GB · 100%

At a glance

Task: Text Generation
Library: gguf
Access: Public
Created: Sep 4, 2026
Updated: Sep 4, 2026
SHA: b3d33231

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Base models

Qwen/Qwen3-0.6B

Model card

From the Hugging Face model README

Qwen3-0.6B GGUF

GGUF quantizations of Qwen/Qwen3-0.6B, covering 30 files (12.8 GB total).

Files

File	Quant	Size	Notes
Qwen3-0.6B-BF16.gguf	`BF16`	1.41 GB	Full precision source. Every quant below is cut from this file.
Qwen3-0.6B-Q8_0.gguf	`Q8_0`	0.75 GB	Effectively lossless. Use when disk and RAM are not the constraint.
Qwen3-0.6B-Q6_K.gguf	`Q6_K`	0.58 GB	Near-lossless; the last stop before quality becomes measurable.
Qwen3-0.6B-Q5_K_M.gguf	`Q5_K_M`	0.51 GB	Very good quality, noticeably smaller than Q6_K.
Qwen3-0.6B-Q5_K_S.gguf	`Q5_K_S`	0.51 GB	Slightly smaller than Q5_K_M for a slight quality cost.
Qwen3-0.6B-Q5_1.gguf	`Q5_1`	0.54 GB	Legacy. Prefer Q5_K_M.
Qwen3-0.6B-Q5_0.gguf	`Q5_0`	0.51 GB	Legacy. Prefer Q5_K_M.
Qwen3-0.6B-Q4_K_M.gguf	`Q4_K_M`	0.45 GB	The usual default. Best quality-per-byte for most people.
Qwen3-0.6B-Q4_K_S.gguf	`Q4_K_S`	0.44 GB	A little smaller than Q4_K_M, a little worse.
Qwen3-0.6B-IQ4_NL.gguf	`IQ4_NL`	0.44 GB	Non-linear 4-bit; good on hardware without fast K-quant kernels.
Qwen3-0.6B-IQ4_XS.gguf	`IQ4_XS`	0.42 GB	Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size.
Qwen3-0.6B-Q4_1.gguf	`Q4_1`	0.47 GB	Legacy. Prefer Q4_K_M.
Qwen3-0.6B-Q4_0.gguf	`Q4_0`	0.44 GB	Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this.
Qwen3-0.6B-Q3_K_L.gguf	`Q3_K_L`	0.41 GB	Small, with real quality loss. Usable when RAM is tight.
Qwen3-0.6B-Q3_K_M.gguf	`Q3_K_M`	0.39 GB	Smaller again; noticeable degradation.
Qwen3-0.6B-IQ3_M.gguf	`IQ3_M`	0.38 GB	Strong at ~3.7bpw, clearly better than Q3_K_M.
Qwen3-0.6B-IQ3_S.gguf	`IQ3_S`	0.36 GB	Slightly smaller than IQ3_M.
Qwen3-0.6B-Q3_K_S.gguf	`Q3_K_S`	0.36 GB	Aggressive. Prefer IQ3_M at a similar size.
Qwen3-0.6B-IQ3_XS.gguf	`IQ3_XS`	0.35 GB	Aggressive but coherent.
Qwen3-0.6B-IQ3_XXS.gguf	`IQ3_XXS`	0.32 GB	Very aggressive; imatrix carries it.
Qwen3-0.6B-Q2_K.gguf	`Q2_K`	0.32 GB	Very small, heavily degraded. For experimentation.
Qwen3-0.6B-IQ2_M.gguf	`IQ2_M`	0.31 GB	The smallest size most people find usable.
Qwen3-0.6B-Q2_K_S.gguf	`Q2_K_S`	0.31 GB	Smaller than Q2_K, requires the imatrix.
Qwen3-0.6B-IQ2_S.gguf	`IQ2_S`	0.30 GB	Below the usual usability line.
Qwen3-0.6B-IQ2_XS.gguf	`IQ2_XS`	0.27 GB	Experimental.
Qwen3-0.6B-IQ2_XXS.gguf	`IQ2_XXS`	0.26 GB	Experimental.
Qwen3-0.6B-Q2_0.gguf	`Q2_0`	0.32 GB	Extreme, group-64. Included for completeness.
Qwen3-0.6B-IQ1_M.gguf	`IQ1_M`	0.25 GB	Extreme. Expect substantial degradation.
Qwen3-0.6B-IQ1_S.gguf	`IQ1_S`	0.24 GB	Extreme. Expect substantial degradation.
Qwen3-0.6B-Q1_0.gguf	`Q1_0`	0.20 GB	Extreme. Included for completeness.

Which one should I download?

Pick the largest file that leaves a couple of gigabytes of headroom on the device you will run it on — the model has to fit in RAM (or VRAM, if you are offloading) alongside the KV cache and the OS.

Plenty of memory: Q6_K or Q8_0.
The usual choice: Q4_K_M.
Tight on memory: IQ4_XS, then IQ3_M, then IQ2_M.
The IQ* files are imatrix-guided and generally beat a Q* file of similar size, at the cost of slightly slower inference on some hardware.

Quantization details

Importance matrix computed with llama-imatrix over 500 rows of Salesforce/wikitext (wikitext-2-raw-v1).
The matrix was computed on the BF16 weights.
K-quants below 6 bit and the whole IQ set are imatrix-guided. Q4_0/Q4_1/Q5_0/Q5_1 are legacy round-to-nearest and ignore it; Q6_K/Q8_0 are near-lossless and do not need it.
All files are cut from the same BF16 GGUF, so differences between them are quantization only.

Usage

llama-cli -hf NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M -p "Hello"

Or download one file and point at it directly:

huggingface-cli download NANI-Nithin/Qwen3-0.6B-GGUF Qwen3-0.6B-Q4_K_M.gguf --local-dir .
llama-cli -m Qwen3-0.6B-Q4_K_M.gguf -p "Hello"

Quantized with llama.cpp by AgentQuantix on 2026-09-04. Licensing follows the base model.

Task: Text Generation
Library: gguf
Created: Sep 4, 2026
Updated: Sep 4, 2026