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Mayonnaise 4in1 02 by kaitchup

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Model Card on HF 🤗: https://huggingface.co/kaitchup/Mayonnaise-4in1-02 

Mayonnaise 4in1 02 Benchmarks

ARC: 73.38 vs 96.7 (so35)-24.1%
HellaSwag: 88.51 vs 95.3 (gpt4)-7.1%
MMLU: 64.89 vs 88.3 (so35)-26.5%
TruthfulQA: 69.04 vs 59 (gpt4)17%
WinoGrande: 84.37 vs 87.5 (gpt4)-3.6%
GSM8K: 71.04 vs 96.4 (so35)-26.3%
LLME Score: 0.13733
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mayonnaise 4in1 02 (kaitchup/Mayonnaise-4in1-02)
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Mayonnaise 4in1 02 Parameters and Internals

Model Type 
Causal
Additional Notes 
The model is a mixture of experts blend created using mergekit with the configuration detailed in the article 'The Mayonnaise: Rank First on the Open LLM Leaderboard with TIES-Merging.'
Supported Languages 
English (fluent)
LLM NameMayonnaise 4in1 02
Repository 🤗https://huggingface.co/kaitchup/Mayonnaise-4in1-02 
Base Model(s)  Mayonnaise 4in1 03   kaitchup/Mayonnaise-4in1-03
Model Size7.2b
Required VRAM14.4 GB
Updated2025-09-23
Maintainerkaitchup
Model Typemistral
Model Files  14.5 GB   4.1 GB   4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
GGUF QuantizationYes
Quantization Typeq4|gguf
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than kaitchup/Mayonnaise-4in1-02.

Rank the Mayonnaise 4in1 02 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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Release v20241124
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