Transformers
GGUF
text-generation-inference
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
Inference Endpoints
imatrix
mradermacher
commited on
Commit
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Parent(s):
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README.md
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/LeroyDyer/LCARS_AI_001
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+
---
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base_model: LeroyDyer/LCARS_AI_001
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datasets:
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- gretelai/synthetic_text_to_sql
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- HuggingFaceTB/cosmopedia
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- teknium/OpenHermes-2.5
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- Open-Orca/SlimOrca
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- Open-Orca/OpenOrca
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- cognitivecomputations/dolphin-coder
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- databricks/databricks-dolly-15k
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- yahma/alpaca-cleaned
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- uonlp/CulturaX
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- mwitiderrick/SwahiliPlatypus
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- swahili
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- Rogendo/English-Swahili-Sentence-Pairs
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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- meta-math/MetaMathQA
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- abacusai/ARC_DPO_FewShot
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- abacusai/MetaMath_DPO_FewShot
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- abacusai/HellaSwag_DPO_FewShot
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- HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset
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- HuggingFaceFW/fineweb
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- occiglot/occiglot-fineweb-v0.5
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- omi-health/medical-dialogue-to-soap-summary
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- keivalya/MedQuad-MedicalQnADataset
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- ruslanmv/ai-medical-dataset
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- Shekswess/medical_llama3_instruct_dataset_short
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- ShenRuililin/MedicalQnA
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- virattt/financial-qa-10K
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- PatronusAI/financebench
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- takala/financial_phrasebank
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- Replete-AI/code_bagel
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- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
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- IlyaGusev/gpt_roleplay_realm
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- rickRossie/bluemoon_roleplay_chat_data_300k_messages
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- jtatman/hypnosis_dataset
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- Hypersniper/philosophy_dialogue
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- Locutusque/function-calling-chatml
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- bible-nlp/biblenlp-corpus
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- DatadudeDev/Bible
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- Helsinki-NLP/bible_para
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- HausaNLP/AfriSenti-Twitter
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- aixsatoshi/Chat-with-cosmopedia
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- HuggingFaceTB/cosmopedia-100k
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- HuggingFaceFW/fineweb-edu
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- m-a-p/CodeFeedback-Filtered-Instruction
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- heliosbrahma/mental_health_chatbot_dataset
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language:
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- en
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- sw
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- ig
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- tw
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- es
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library_name: transformers
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license: apache-2.0
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quantized_by: mradermacher
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- mistral
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- trl
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- chemistry
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- biology
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- legal
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- art
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- music
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- finance
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- code
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- medical
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- not-for-all-audiences
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- merge
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- climate
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- chain-of-thought
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- tree-of-knowledge
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- forest-of-thoughts
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- visual-spacial-sketchpad
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- alpha-mind
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- knowledge-graph
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- entity-detection
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- encyclopedia
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- wikipedia
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- stack-exchange
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- Reddit
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- Cyber-series
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- MegaMind
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- Cybertron
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- SpydazWeb
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- Spydaz
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- LCARS
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- star-trek
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- mega-transformers
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- Mulit-Mega-Merge
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- Multi-Lingual
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- Afro-Centric
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- African-Model
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- Ancient-One
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/LeroyDyer/LCARS_AI_001
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<!-- provided-files -->
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static quants are available at https://huggingface.co/mradermacher/LCARS_AI_001-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [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 |
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| [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 |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
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| [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 |
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| [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 |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
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| [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 |
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| [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* |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
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| [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 |
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| [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 |
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| [GGUF](https://huggingface.co/mradermacher/LCARS_AI_001-i1-GGUF/resolve/main/LCARS_AI_001.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
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| [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 |
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| [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 |
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| [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 |
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| [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 | |
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| [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 | |
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| [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 |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
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<!-- end -->
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