Instructions to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with Ollama:
ollama run hf.co/mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF to start chatting
- Docker Model Runner
How to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/SpydazWeb_AI_LCARS_Humanization_003-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SpydazWeb_AI_LCARS_Humanization_003-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
base_model: LeroyDyer/SpydazWeb_AI_LCARS_Humanization_003
datasets:
- gretelai/synthetic_text_to_sql
- HuggingFaceTB/cosmopedia
- teknium/OpenHermes-2.5
- Open-Orca/SlimOrca
- Severian/Internal-Knowledge-Map
- Open-Orca/OpenOrca
- cognitivecomputations/dolphin-coder
- databricks/databricks-dolly-15k
- yahma/alpaca-cleaned
- uonlp/CulturaX
- mwitiderrick/SwahiliPlatypus
- NexusAI-tddi/OpenOrca-tr-1-million-sharegpt
- Vezora/Open-Critic-GPT
- verifiers-for-code/deepseek_plans_test
- meta-math/MetaMathQA
- KbsdJames/Omni-MATH
- 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
- xz56/react-llama
- BeIR/hotpotqa
- YBXL/medical_book_train_filtered
- SkunkworksAI/reasoning-0.01
- THUDM/LongWriter-6k
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/Code-Functions-Level-Cyber
- WhiteRabbitNeo/Code-Functions-Level-General
language:
- en
- sw
- ig
- so
- es
- ca
- xh
- zu
- ha
- tw
- af
- hi
- bm
- su
library_name: transformers
license: mit
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
static quants of https://huggingface.co/LeroyDyer/SpydazWeb_AI_LCARS_Humanization_003
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SpydazWeb_AI_LCARS_Humanization_003-i1-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 | Q2_K | 2.8 | |
| GGUF | Q3_K_S | 3.3 | |
| GGUF | Q3_K_M | 3.6 | lower quality |
| GGUF | Q3_K_L | 3.9 | |
| GGUF | IQ4_XS | 4.0 | |
| GGUF | Q4_K_S | 4.2 | fast, recommended |
| GGUF | Q4_K_M | 4.5 | fast, recommended |
| GGUF | Q5_K_S | 5.1 | |
| GGUF | Q5_K_M | 5.2 | |
| GGUF | Q6_K | 6.0 | very good quality |
| GGUF | Q8_0 | 7.8 | fast, best quality |
| GGUF | f16 | 14.6 | 16 bpw, overkill |
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.
