--- base_model: LeroyDyer/LCARS_AI_001 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 - tw - es 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 weighted/imatrix quants of https://huggingface.co/LeroyDyer/LCARS_AI_001 static quants are available at https://huggingface.co/mradermacher/LCARS_AI_001-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/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-Q6_K.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): ![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. Additional thanks to [@nicoboss](https://huggingface.co/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.