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PiVoT MoE by maywell

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  Autotrain compatible   Conversational   Endpoints compatible   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/maywell/PiVoT-MoE 

PiVoT MoE Benchmarks

ARC: 63.91 vs 96.7 (so35)-33.9%
HellaSwag: 83.52 vs 95.3 (gpt4)-12.4%
MMLU: 60.71 vs 88.3 (so35)-31.2%
TruthfulQA: 54.64 vs 59 (gpt4)-7.4%
WinoGrande: 76.32 vs 87.5 (gpt4)-12.8%
GSM8K: 39.12 vs 96.4 (so35)-59.4%
LLME Score: 0.1614
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
PiVoT MoE (maywell/PiVoT-MoE)
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PiVoT MoE Parameters and Internals

Model Type 
roleplaying, Mixture of Experts (MoE)
Additional Notes 
Model designed specifically for roleplaying purposes using Mixture of Experts architecture.
Training Details 
Methodology:
Mixture of Experts (MoE) technique
Model Architecture:
Based on PiVoT-10.7B-Mistral-v0.2-RP with MoE architecture
Input Output 
Input Format:
Alpaca (ChatML works)
LLM NamePiVoT MoE
Repository 🤗https://huggingface.co/maywell/PiVoT-MoE 
Model Size36.1b
Required VRAM72.3 GB
Updated2025-09-23
Maintainermaywell
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
Licensecc-by-nc-4.0
Context Length32768
Model Max Length32768
Transformers Version4.36.1
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the PiVoT MoE

Model
Likes
Downloads
VRAM
PiVoT MoE GGUF919712 GB
PiVoT MoE AWQ21019 GB
PiVoT MoE GPTQ1718 GB

Best Alternatives to PiVoT MoE

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Context / RAM
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Likes
Umbra V3 MoE 4x11b 2ex32K / 72.3 GB2864
Umbra V3 MoE 4x11b 2ex32K / 72.3 GB54
Umbra V3 MoE 4x11b32K / 72.3 GB55
Umbra V2.1 MoE 4x10.732K / 72.3 GB66
Mixolar 4x7b4K / 72.3 GB97803
Smartsolmix 4x10.7B V14K / 72.3 GB18580
Orca SOLAR 4x10.7B4K / 72.3 GB17380
MetaModel MoE4K / 72.3 GB19140
SOLARC MoE 10.7Bx44K / 144.7 GB19179
Frankenstein MoE En 10.7Bx44K / 72.3 GB19150
Note: green Score (e.g. "73.2") means that the model is better than maywell/PiVoT-MoE.

Rank the PiVoT 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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