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MetaModel MoE by gagan3012

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  Autotrain compatible   Conversational   Endpoints compatible   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/gagan3012/MetaModel_moe 

MetaModel MoE Benchmarks

ARC: 71.08 vs 96.7 (so35)-26.5%
HellaSwag: 88.39 vs 95.3 (gpt4)-7.3%
MMLU: 66.31 vs 88.3 (so35)-24.9%
TruthfulQA: 71.82 vs 59 (gpt4)21.7%
WinoGrande: 83.5 vs 87.5 (gpt4)-4.6%
GSM8K: 64.59 vs 96.4 (so35)-33%
LLME Score: 0.15511
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
MetaModel MoE (gagan3012/MetaModel_moe)
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MetaModel MoE Parameters and Internals

Model Type 
Mixture of Experts (MoE)
LLM NameMetaModel MoE
Repository 🤗https://huggingface.co/gagan3012/MetaModel_moe 
Model Size36.1b
Required VRAM72.3 GB
Updated2025-09-23
Maintainergagan3012
Model Typemixtral
Model Files  9.9 GB: 1-of-8   10.0 GB: 2-of-8   10.0 GB: 3-of-8   10.0 GB: 4-of-8   10.0 GB: 5-of-8   10.0 GB: 6-of-8   10.0 GB: 7-of-8   2.4 GB: 8-of-8
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.36.2
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 gagan3012/MetaModel_moe.

Rank the MetaModel MoE 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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