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Beyonder 4x7B V3 by mlabonne

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Beyonder 4x7B V3 Benchmarks

MMLU Pro: 16.81
GPQA: 4.7
MUSR: 8.93
BBH: 24.56
IFEval: 56.08 vs 88 (so35)-36.3%
ARC: 71.67 vs 96.7 (so35)-25.9%
HellaSwag: 88.86 vs 95.3 (gpt4)-6.8%
MMLU: 64.87 vs 88.3 (so35)-26.5%
TruthfulQA: 74.44 vs 59 (gpt4)26.2%
WinoGrande: 83.5 vs 87.5 (gpt4)-4.6%
GSM8K: 70.58 vs 96.4 (so35)-26.8%
MATH Lvl 5: 4.76
LLME Score: 0.31259
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Beyonder 4x7B V3 Parameters and Internals

LLM NameBeyonder 4x7B V3
Repository 🤗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
Updated2024-10-17
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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Release v2024072803
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