base_model, language, library_name, license, license_link, license_name, quantized_by, tags

base_model	language	library_name	license	license_link	license_name	quantized_by	tags
raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE	en	transformers	other	LICENSE	llama3	mradermacher	facebook meta pytorch llama llama-3 moe code

About

weighted/imatrix quants of https://huggingface.co/raincandy-u/Llama-3-Aplite-Instruct-4x8B-MoE

static quants are available at https://huggingface.co/mradermacher/Llama-3-Aplite-Instruct-4x8B-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	i1-IQ1_S	5.5	for the desperate
GGUF	i1-IQ1_M	6.0	mostly desperate
GGUF	i1-IQ2_XXS	6.9
GGUF	i1-IQ2_XS	7.6
GGUF	i1-IQ2_S	7.8
GGUF	i1-IQ2_M	8.5
GGUF	i1-Q2_K	9.4	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	9.8	lower quality
GGUF	i1-IQ3_XS	10.5
GGUF	i1-Q3_K_S	11.0	IQ3_XS probably better
GGUF	i1-IQ3_S	11.1	beats Q3_K*
GGUF	i1-IQ3_M	11.2
GGUF	i1-Q3_K_M	12.2	IQ3_S probably better
GGUF	i1-Q3_K_L	13.1	IQ3_M probably better
GGUF	i1-IQ4_XS	13.5
GGUF	i1-Q4_0	14.3	fast, low quality
GGUF	i1-Q4_K_S	14.4	optimal size/speed/quality
GGUF	i1-Q4_K_M	15.3	fast, recommended
GGUF	i1-Q5_K_S	17.3
GGUF	i1-Q5_K_M	17.8
GGUF	i1-Q6_K	20.6	practically like static Q6_K

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.