Beyonder 4x7B V3 by mlabonne

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Beyonder 4x7B V3 Benchmarks

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Beyonder 4x7B V3 Parameters and Internals

Model Type 
Mixture of Experts (MoE), text-generation
Use Cases 
Applications:
chat, programming, storytelling, mathematics
Additional Notes 
The model leverages four experts, each specializing in specific task types for well-rounded capabilities.
Input Output 
Input Format:
Mistral Instruct chat template
Accepted Modalities:
text
Performance Tips:
Use Mistral Instruct chat template for optimal results, tweak inference parameters if using SillyTavern.
LLM NameBeyonder 4x7B V3
Repository ๐Ÿค—https://huggingface.co/mlabonne/Beyonder-4x7B-v3 
Base Model(s)  AlphaMonarch 7B   beowolx/CodeNinja-1.0-OpenChat-7B   Kunoichi DPO V2 7B   NeuralDaredevil 7B   mlabonne/AlphaMonarch-7B   beowolx/CodeNinja-1.0-OpenChat-7B   SanjiWatsuki/Kunoichi-DPO-v2-7B   mlabonne/NeuralDaredevil-7B
Model Size24.2b
Required VRAM48.3 GB
Updated2024-12-04
Maintainermlabonne
Model Typemixtral
Model Files  9.9 GB: 1-of-5   9.9 GB: 2-of-5   10.0 GB: 3-of-5   10.0 GB: 4-of-5   8.5 GB: 5-of-5
Model ArchitectureMixtralForCausalLM
Licensecc-by-nc-4.0
Context Length32768
Model Max Length32768
Transformers Version4.39.0
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typefloat16
Beyonder 4x7B V3 (mlabonne/Beyonder-4x7B-v3)

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Note: green Score (e.g. "73.2") means that the model is better than mlabonne/Beyonder-4x7B-v3.

Rank the Beyonder 4x7B V3 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124