base_model, language, library_name, license, mradermacher, quantized_by, tags

base_model	language	library_name	license	mradermacher	quantized_by	tags
DavidAU/Qwen3-MOE-2x4B-8B-Jan-Nano-Instruct-II	en fr zh de	transformers	apache-2.0	readme_rev 1	mradermacher	programming code generation code codeqwen programming code generation code codeqwen moe coding coder qwen2 chat qwen qwen-coder chat qwen qwen-coder moe mixture of experts 2 experts 2 active experts 40k context qwen3 finetune qwen3_moe merge

About

static quants of https://huggingface.co/DavidAU/Qwen3-MOE-2x4B-8B-Jan-Nano-Instruct-II

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-MOE-2x4B-8B-Jan-Nano-Instruct-II-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	2.7
GGUF	Q3_K_S	3.1
GGUF	Q3_K_M	3.5	lower quality
GGUF	Q3_K_L	3.7
GGUF	IQ4_XS	3.8
GGUF	Q4_K_S	4.0	fast, recommended
GGUF	Q4_K_M	4.2	fast, recommended
GGUF	Q5_K_S	4.8
GGUF	Q5_K_M	4.9
GGUF	Q6_K	5.6	very good quality
GGUF	Q8_0	7.2	fast, best quality
GGUF	f16	13.5	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.