mradermacher/SpydazWeb_AI_HumanAI_008_ChatQA-i1-GGUF

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basemodel: LeroyDyer/SpydazWebAIHumanAI008_ChatQA datasets:

• neoneye/base64-decode-v2
• neoneye/base64-encode-v1
• VuongQuoc/Chemistrytexttoimage
• Kamizuru00/diagramimagetotext
• LeroyDyer/ChemistrytexttoimageBASE64
• LeroyDyer/AudioCaps-SpectrogramstoBase64
• LeroyDyer/winogroudtexttoimagetBASE64
• LeroyDyer/charttexttoBase64
• LeroyDyer/diagramimagetotextBASE64
• mekaneeky/saltm2e153_instruction
• mekaneeky/SALT-languages-bible
• xz56/react-llama
• BeIR/hotpotqa
• arcee-ai/agent-data

language:

• en
• sw
• ig
• so
• es
• ca
• xh
• zu
• ha
• tw
• af
• hi
• bm
• su

library_name: transformers license: apache-2.0 quantized_by: mradermacher tags:

• text-generation-inference
• transformers
• unsloth
• mistral
• MistralStar
• MistralQuiet
• Mistral
• Mixtral
• Question-Answer
• Token-Classification
• Sequence-Classification
• SpydazWeb-AI
• chemistry
• biology
• legal
• code
• climate
• medical
• LCARSAIStarTrek_Computer
• text-generation-inference
• chain-of-thought
• tree-of-knowledge
• forest-of-thoughts
• visual-spacial-sketchpad
• alpha-mind
• knowledge-graph
• entity-detection
• encyclopedia
• wikipedia
• stack-exchange
• Reddit
• Cyber-series
• MegaMind
• Cybertron
• SpydazWeb
• Spydaz
• LCARS
• star-trek
• mega-transformers
• Mulit-Mega-Merge
• Multi-Lingual
• Afro-Centric
• African-Model
• Ancient-One

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About

weighted/imatrix quants of https://huggingface.co/LeroyDyer/SpydazWebAIHumanAI008ChatQA

static quants are available at https://huggingface.co/mradermacher/SpydazWebAIHumanAI008ChatQA-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	1.7	for the desperate
GGUF	i1-IQ1_M	1.9	mostly desperate
GGUF	i1-IQ2_XXS	2.1
GGUF	i1-IQ2_XS	2.3
GGUF	i1-IQ2_S	2.4
GGUF	i1-IQ2_M	2.6
GGUF	i1-Q2K	2.8	IQ3XXS probably better
GGUF	i1-IQ3_XXS	2.9	lower quality
GGUF	i1-IQ3_XS	3.1
GGUF	i1-Q3KS	3.3	IQ3XS probably better
GGUF	i1-IQ3S	3.3	beats Q3K*
GGUF	i1-IQ3_M	3.4
GGUF	i1-Q3KM	3.6	IQ3S probably better
GGUF	i1-Q3KL	3.9	IQ3M probably better
GGUF	i1-IQ4_XS	4.0
GGUF	i1-Q404_4	4.2	fast on arm, low quality
GGUF	i1-Q404_8	4.2	fast on arm+i8mm, low quality
GGUF	i1-Q408_8	4.2	fast on arm+sve, low quality
GGUF	i1-Q4_0	4.2	fast, low quality
GGUF	i1-Q4K_S	4.2	optimal size/speed/quality
GGUF	i1-Q4K_M	4.5	fast, recommended
GGUF	i1-Q5K_S	5.1
GGUF	i1-Q5K_M	5.2
GGUF	i1-Q6K	6.0	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.