Edit model card

About

weighted/imatrix quants of https://huggingface.co/mlabonne/Beyonder-4x7B-v2

static quants are available at https://huggingface.co/mradermacher/Beyonder-4x7B-v2-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 5.3 for the desperate
GGUF i1-IQ2_XXS 6.7
GGUF i1-IQ2_XS 7.4
GGUF i1-IQ2_S 7.6
GGUF i1-IQ2_M 8.3
GGUF i1-Q2_K 9.1 IQ3_XXS probably better
GGUF i1-IQ3_XXS 9.6 lower quality
GGUF i1-IQ3_XS 10.1
GGUF i1-Q3_K_S 10.7 IQ3_XS probably better
GGUF i1-IQ3_S 10.7 beats Q3_K*
GGUF i1-IQ3_M 10.9
GGUF i1-Q3_K_M 11.8 IQ3_S probably better
GGUF i1-Q3_K_L 12.8 IQ3_M probably better
GGUF i1-Q4_K_S 14.0 optimal size/speed/quality
GGUF i1-Q4_K_M 14.9 fast, recommended
GGUF i1-Q5_K_S 16.9
GGUF i1-Q5_K_M 17.4
GGUF i1-Q6_K 20.1 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
613
GGUF
Model size
24.2B params
Architecture
llama

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

Inference API
Unable to determine this model’s pipeline type. Check the docs .

Model tree for mradermacher/Beyonder-4x7B-v2-i1-GGUF

Quantized
(5)
this model