---
base_model: LeroyDyer/LCARS_AI_014
datasets:
- gretelai/synthetic_text_to_sql
- HuggingFaceTB/cosmopedia
- teknium/OpenHermes-2.5
- Open-Orca/SlimOrca
- Open-Orca/OpenOrca
- cognitivecomputations/dolphin-coder
- databricks/databricks-dolly-15k
- yahma/alpaca-cleaned
- uonlp/CulturaX
- mwitiderrick/SwahiliPlatypus
- swahili
- Rogendo/English-Swahili-Sentence-Pairs
- ise-uiuc/Magicoder-Evol-Instruct-110K
- meta-math/MetaMathQA
- abacusai/ARC_DPO_FewShot
- abacusai/MetaMath_DPO_FewShot
- abacusai/HellaSwag_DPO_FewShot
- HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset
- HuggingFaceFW/fineweb
- occiglot/occiglot-fineweb-v0.5
- omi-health/medical-dialogue-to-soap-summary
- keivalya/MedQuad-MedicalQnADataset
- ruslanmv/ai-medical-dataset
- Shekswess/medical_llama3_instruct_dataset_short
- ShenRuililin/MedicalQnA
- virattt/financial-qa-10K
- PatronusAI/financebench
- takala/financial_phrasebank
- Replete-AI/code_bagel
- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
- IlyaGusev/gpt_roleplay_realm
- rickRossie/bluemoon_roleplay_chat_data_300k_messages
- jtatman/hypnosis_dataset
- Hypersniper/philosophy_dialogue
- Locutusque/function-calling-chatml
- bible-nlp/biblenlp-corpus
- DatadudeDev/Bible
- Helsinki-NLP/bible_para
- HausaNLP/AfriSenti-Twitter
- aixsatoshi/Chat-with-cosmopedia
- HuggingFaceTB/cosmopedia-100k
- HuggingFaceFW/fineweb-edu
- m-a-p/CodeFeedback-Filtered-Instruction
- heliosbrahma/mental_health_chatbot_dataset
language:
- en
- sw
- ig
- so
- es
- ca
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- chemistry
- biology
- legal
- art
- music
- finance
- code
- medical
- not-for-all-audiences
- merge
- climate
- 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

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static quants of https://huggingface.co/LeroyDyer/LCARS_AI_014

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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q2_K.gguf) | Q2_K | 2.8 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.IQ3_XS.gguf) | IQ3_XS | 3.1 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q3_K_S.gguf) | Q3_K_S | 3.3 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.IQ3_M.gguf) | IQ3_M | 3.4 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q3_K_L.gguf) | Q3_K_L | 3.9 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.IQ4_XS.gguf) | IQ4_XS | 4.0 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q5_K_S.gguf) | Q5_K_S | 5.1 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q5_K_M.gguf) | Q5_K_M | 5.2 |  |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_014-GGUF/resolve/main/LCARS_AI_014.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.

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