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Sensualize Mixtral AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Base model:quantized:sao10k/se... Base model:sao10k/sensualize-m... Dataset:nobodyexistsontheinter...   Mixtral   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/Sensualize-Mixtral-AWQ 

Sensualize Mixtral AWQ Benchmarks

LLME Score: 0.15991
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Sensualize Mixtral AWQ (TheBloke/Sensualize-Mixtral-AWQ)

Sensualize Mixtral AWQ Parameters and Internals

Model Type 
mixtral
Additional Notes 
Experimental model, trained using Alpaca format. Roleplay based model, specifically ERP type, with varied performance depending on prompts. Recommended settings include Universal-Light or Universal-Creative in SillyTavern.
Training Details 
Data Sources:
Randomised subset of Full120k - 60K Samples, Own NSFW Instruct & De-Alignment Data
Data Volume:
80M Tokens
Methodology:
Alpaca format using Charles Goddard's ZLoss and Megablocks-based fork of transformers.
Training Time:
12 hours
Hardware Used:
2xA100s at batch size 5, grad 5
Input Output 
Input Format:
### Instruction: {system_message} ### Input: {prompt} ### Response:
Output Format:
Response
Performance Tips:
With the right settings this model can shine. I recommend Universal-Light or Universal-Creative in SillyTavern.
LLM NameSensualize Mixtral AWQ
Repository 🤗https://huggingface.co/TheBloke/Sensualize-Mixtral-AWQ 
Model NameSensualize Mixtral
Model CreatorSaofiq
Base Model(s)  Sensualize Mixtral Bf16   Sao10K/Sensualize-Mixtral-bf16
Model Size6.5b
Required VRAM24.7 GB
Updated2025-02-05
MaintainerTheBloke
Model Typemixtral
Model Files  10.0 GB: 1-of-3   10.0 GB: 2-of-3   4.7 GB: 3-of-3
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMixtralForCausalLM
Licensecc-by-nc-4.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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

Rank the Sensualize Mixtral AWQ 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  
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Multi-Language Support and Translation  

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Release v20241227
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