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Sensualize Mixtral GGUF by TheBloke

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Sensualize Mixtral GGUF is an open-source language model by TheBloke. Features: LLM, VRAM: 15.6GB, License: cc-by-nc-4.0, Quantized, LLM Explorer Score: 0.12.

Base model:quantized:sao10k/se... Base model:sao10k/sensualize-m... Dataset:nobodyexistsontheinter...   Gguf   Mixtral   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/Sensualize-Mixtral-GGUF 

Sensualize Mixtral GGUF Benchmarks

LLME Score: 0.11585
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Sensualize Mixtral GGUF (TheBloke/Sensualize-Mixtral-GGUF)
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Sensualize Mixtral GGUF Parameters and Internals

Model Type 
mixtral
Use Cases 
Primary Use Cases:
Roleplay-based models, specifically ERP
Limitations:
Finicky with settings
Additional Notes 
Trained using 80M tokens over 1 epoch, with ZLoss and Megablocks-based fork of transformers. Experimental and finicky model, recommended to use Universal-Light or Universal-Creative settings in SillyTavern for improved performance.
Training Details 
Data Sources:
NobodyExistsOnTheInternet/full120k, My own NSFW Instruct & De-Alignment Data
Data Volume:
80M tokens
Methodology:
Trained on ZLoss and Megablocks-based fork of transformers, using Alpaca format
Training Time:
12 hours on 2xA100s
Hardware Used:
2xA100s
Input Output 
Input Format:
### Instruction: {system_message} ### Input: {prompt} ### Response:
LLM NameSensualize Mixtral GGUF
Repository 🤗https://huggingface.co/TheBloke/Sensualize-Mixtral-GGUF 
Model NameSensualize Mixtral
Model CreatorSaofiq
Base Model(s)  Sensualize Mixtral Bf16   Sao10K/Sensualize-Mixtral-bf16
Required VRAM15.6 GB
Updated2026-05-10
MaintainerTheBloke
Model Typemixtral
Model Files  15.6 GB   20.4 GB   26.4 GB   26.4 GB   32.2 GB   32.2 GB   38.4 GB   49.6 GB
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licensecc-by-nc-4.0

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Sensualize-Mixtral-GGUF.

Rank the Sensualize Mixtral GGUF Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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