mradermacher/Faro-Yi-34B-200K-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/wenbopan/Faro-Yi-34B-200K

This uses my "quarter" training set of 40k tokens as the model overflowed after 25k tokens with the standard set.

static quants are available at https://huggingface.co/mradermacher/Faro-Yi-34B-200K-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	8.2	for the desperate
GGUF	i1-IQ1_M	8.9	mostly desperate
GGUF	i1-IQ2_XXS	10.0
GGUF	i1-IQ2_XS	11.0
GGUF	i1-IQ2_S	11.6
GGUF	i1-IQ2_M	12.5
GGUF	i1-Q2_K	13.5	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	14.0	lower quality
GGUF	i1-IQ3_XS	14.9
GGUF	i1-Q3_K_S	15.6	IQ3_XS probably better
GGUF	i1-IQ3_S	15.7	beats Q3_K*
GGUF	i1-IQ3_M	16.2
GGUF	i1-Q3_K_M	17.3	IQ3_S probably better
GGUF	i1-Q3_K_L	18.8	IQ3_M probably better
GGUF	i1-IQ4_XS	19.1
GGUF	i1-Q4_0	20.2	fast, low quality
GGUF	i1-Q4_K_S	20.2	optimal size/speed/quality
GGUF	i1-Q4_K_M	21.3	fast, recommended
GGUF	i1-Q5_K_S	24.3
GGUF	i1-Q5_K_M	25.0
GGUF	i1-Q6_K	28.9	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.