README.md

---
base_model: LeroyDyer/SpydazWebAI_Human_AGI
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
language:
- en
- sw
- ig
- so
- es
- ca
- xh
- zu
- ha
- tw
- af
- hi
- bm
- su
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
- 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

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
static quants of https://huggingface.co/LeroyDyer/SpydazWebAI_Human_AGI

<!-- provided-files -->

***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SpydazWebAI_Human_AGI-GGUF).***

weighted/imatrix quants are available at https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q2_K.gguf) | Q2_K | 2.8 |  |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q3_K_S.gguf) | Q3_K_S | 3.3 |  |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q3_K_L.gguf) | Q3_K_L | 3.9 |  |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.IQ4_XS.gguf) | IQ4_XS | 4.0 |  |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q5_K_S.gguf) | Q5_K_S | 5.1 |  |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q5_K_M.gguf) | Q5_K_M | 5.2 |  |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SpydazWebAI_Human_AGI-GGUF/resolve/main/SpydazWebAI_Human_AGI.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |

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

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

<!-- end -->