mradermacher/Ling-flash-2.0-i1-GGUF

---

base_model: inclusionAI/Ling-flash-2.0 language:

• en

library_name: transformers license: mit mradermacher: readme_rev: 1 quantized_by: mradermacher

---

About

weighted/imatrix quants of https://huggingface.co/inclusionAI/Ling-flash-2.0

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/Ling-flash-2.0-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	imatrix	0.4	imatrix file (for creating your own quants)
GGUF	i1-IQ1_S	21.1	for the desperate
GGUF	i1-IQ1_M	23.4	mostly desperate
GGUF	i1-IQ2_XXS	27.2
GGUF	i1-IQ2_XS	30.3
GGUF	i1-IQ2_S	30.7
GGUF	i1-IQ2_M	33.8
GGUF	i1-Q2K_S	35.1	very low quality
GGUF	i1-Q2K	37.8	IQ3XXS probably better
GGUF	i1-IQ3_XXS	39.9	lower quality
GGUF	i1-IQ3_XS	42.3
GGUF	i1-IQ3S	44.7	beats Q3K*
GGUF	i1-Q3KS	44.7	IQ3XS probably better
GGUF	i1-IQ3_M	45.3
GGUF	i1-Q3KM	49.4	IQ3S probably better
PART 1 PART 2	i1-Q3KL	53.5	IQ3_M probably better
PART 1 PART 2	i1-IQ4_XS	55.1
PART 1 PART 2	i1-Q4_0	58.5	fast, low quality
PART 1 PART 2	i1-Q4KS	58.7	optimal size/speed/quality
PART 1 PART 2	i1-Q4KM	62.5	fast, recommended
PART 1 PART 2	i1-Q4_1	64.6
PART 1 PART 2	i1-Q5KS	71.0
PART 1 PART 2	i1-Q5KM	73.3
PART 1 PART 2	i1-Q6K	84.6	practically like static Q6K

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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.