base_model: LeroyDyer/SpydazWeb_AI_HumanAI_008_ChatQA
datasets:
- neoneye/base64-decode-v2
- neoneye/base64-encode-v1
- VuongQuoc/Chemistry_text_to_image
- Kamizuru00/diagram_image_to_text
- LeroyDyer/Chemistry_text_to_image_BASE64
- LeroyDyer/AudioCaps-Spectrograms_to_Base64
- LeroyDyer/winogroud_text_to_imaget_BASE64
- LeroyDyer/chart_text_to_Base64
- LeroyDyer/diagram_image_to_text_BASE64
- mekaneeky/salt_m2e_15_3_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
- Mistral_Star
- Mistral_Quiet
- Mistral
- Mixtral
- Question-Answer
- Token-Classification
- Sequence-Classification
- SpydazWeb-AI
- chemistry
- biology
- legal
- code
- climate
- medical
- LCARS_AI_StarTrek_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
About
weighted/imatrix quants of https://huggingface.co/LeroyDyer/SpydazWeb_AI_HumanAI_008_ChatQA
static quants are available at https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAI_008_ChatQA-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-Q2_K | 2.8 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 2.9 | lower quality |
| GGUF | i1-IQ3_XS | 3.1 | |
| GGUF | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| GGUF | i1-IQ3_S | 3.3 | beats Q3_K* |
| GGUF | i1-IQ3_M | 3.4 | |
| GGUF | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 4.0 | |
| GGUF | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
| GGUF | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
| GGUF | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
| GGUF | i1-Q4_0 | 4.2 | fast, low quality |
| GGUF | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 4.5 | fast, recommended |
| GGUF | i1-Q5_K_S | 5.1 | |
| GGUF | i1-Q5_K_M | 5.2 | |
| GGUF | i1-Q6_K | 6.0 | 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. 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.