Edit model card

About

weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/Ultra-WizardLM-120B-v0.1

static quants are available at https://huggingface.co/mradermacher/Ultra-WizardLM-120B-v0.1-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-IQ2_XXS 32.2
GGUF i1-IQ2_XS 35.8
GGUF i1-IQ2_S 37.6
GGUF i1-IQ2_M 40.9
GGUF i1-Q2_K 44.6 IQ3_XXS probably better
GGUF i1-IQ3_XXS 46.6 lower quality
GGUF i1-IQ3_XS 49.4
PART 1 PART 2 i1-Q3_K_S 52.2 IQ3_XS probably better
PART 1 PART 2 i1-IQ3_S 52.4 beats Q3_K*
PART 1 PART 2 i1-IQ3_M 54.2
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 64.6
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 83.2
PART 1 PART 2 i1-Q5_K_M 85.4
PART 1 PART 2 PART 3 i1-Q6_K 99.1 practically like static Q6_K

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

image.png

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.

Downloads last month
142
GGUF
+2
Unable to determine this model’s pipeline type. Check the docs .

Quantized from