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Mayonnaise 4in1 022 by kaitchup

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Mayonnaise 4in1 022 is an open-source language model by kaitchup. Features: 7.2b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, HF Score: 75.6, LLM Explorer Score: 0.12, Arc: 72.9, HellaSwag: 88.6, MMLU: 64.9, TruthfulQA: 71.7, WinoGrande: 84.7, GSM8K: 70.5.

  En   Endpoints compatible   Merge   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/kaitchup/Mayonnaise-4in1-022 

Mayonnaise 4in1 022 Benchmarks

ARC: 72.87 vs 96.7 (so35)-24.6%
HellaSwag: 88.63 vs 95.3 (gpt4)-7%
MMLU: 64.93 vs 88.3 (so35)-26.5%
TruthfulQA: 71.73 vs 59 (gpt4)21.6%
WinoGrande: 84.69 vs 87.5 (gpt4)-3.2%
GSM8K: 70.51 vs 96.4 (so35)-26.9%
LLME Score: 0.1219
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mayonnaise 4in1 022 (kaitchup/Mayonnaise-4in1-022)
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Mayonnaise 4in1 022 Parameters and Internals

Model Type 
Causal
Additional Notes 
Warning: This model is ranked first on the Open LLM Leaderboard among the 7B models as of January 28th, 2024, but note that it was produced from merges without fine-tuning and it was not confirmed whether any of the models were trained on evaluation benchmarks.
Supported Languages 
en (proficient)
Training Details 
Methodology:
Created with mergekit with the specified configuration: models: mncai/mistral-7b-dpo-v5, FelixChao/WestSeverus-7B-DPO-v2, BarryFutureman/NeuralTurdusVariant1-7B with parameters: density: 0.5, weight: 0.3 and 0.5, merge_method: ties, base_model: mncai/mistral-7b-dpo-v5, parameters: normalize: true, dtype: float16
LLM NameMayonnaise 4in1 022
Repository 🤗https://huggingface.co/kaitchup/Mayonnaise-4in1-022 
Model Size7.2b
Required VRAM14.4 GB
Updated2026-04-20
Maintainerkaitchup
Model Typemistral
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
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-4in1-022.

Rank the Mayonnaise 4in1 022 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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