mradermacher/Samantha-120b-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/cognitivecomputations/Samantha-120b

the weights were calculated using 164k semi-random english tokens.

static quants are available at https://huggingface.co/mradermacher/Samantha-120b-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	25.7	for the desperate
GGUF	i1-IQ1_M	27.8	mostly desperate
GGUF	i1-IQ2_XXS	32.2
GGUF	i1-IQ2_XS	35.8
GGUF	i1-IQ2_S	37.2
GGUF	i1-IQ2_M	40.5
GGUF	i1-Q2_K	44.6	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	47.3	lower quality
GGUF	i1-IQ3_XS	49.3
PART 1 PART 2	i1-Q3_K_XS	49.3
PART 1 PART 2	i1-IQ3_S	52.1	beats Q3_K*
PART 1 PART 2	i1-Q3_K_S	52.2	IQ3_XS probably better
PART 1 PART 2	i1-IQ3_M	53.8
PART 1 PART 2	i1-Q3_K_M	58.2	IQ3_S probably better
PART 1 PART 2	i1-Q3_K_L	63.4	IQ3_M probably better
PART 1 PART 2	i1-IQ4_XS	64.3
PART 1 PART 2	i1-Q4_0	68.1	fast, low quality
PART 1 PART 2	i1-Q4_K_S	68.7	optimal size/speed/quality
PART 1 PART 2	i1-Q4_K_M	72.6	fast, recommended
PART 1 PART 2	i1-Q5_K_S	82.9
PART 1 PART 2	i1-Q5_K_M	85.1
PART 1 PART 2	i1-Q6_K	98.8	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.