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Mayo by Eric111

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  Merged Model   Autotrain compatible   Conversational   Endpoints compatible   Mistral   Mlabonne/neuralbeagle14-7b   Openchat/openchat-3.5-0106   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Eric111/Mayo 

Mayo Benchmarks

ARC: 70.14 vs 96.7 (so35)-27.5%
HellaSwag: 86.27 vs 95.3 (gpt4)-9.5%
MMLU: 65.58 vs 88.3 (so35)-25.7%
TruthfulQA: 60.93 vs 59 (gpt4)3.3%
WinoGrande: 82.16 vs 87.5 (gpt4)-6.1%
GSM8K: 73.39 vs 96.4 (so35)-23.9%
LLME Score: 0.17321
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mayo (Eric111/Mayo)

Mayo Parameters and Internals

Additional Notes 
Mayo is a model created by merging two models: mlabonne/NeuralBeagle14-7B and openchat/openchat-3.5-0106 using a tool called mergekit. Specific layer ranges and parameters for the merge were used.
LLM NameMayo
Repository 🤗https://huggingface.co/Eric111/Mayo 
Merged ModelYes
Model Size7b
Required VRAM14.4 GB
Updated2025-02-22
MaintainerEric111
Model Typemistral
Model Files  1.9 GB: 1-of-8   1.9 GB: 2-of-8   1.9 GB: 3-of-8   1.9 GB: 4-of-8   2.0 GB: 5-of-8   1.9 GB: 6-of-8   2.0 GB: 7-of-8   0.9 GB: 8-of-8
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Eric111/Mayo.

Rank the Mayo Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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 v20241227
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