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Beyonder 4x7B V2 by shadowml

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Model Card on HF 🤗: https://huggingface.co/shadowml/Beyonder-4x7B-v2 

Beyonder 4x7B V2 Benchmarks

ARC: 68.77 vs 96.7 (so35)-28.9%
HellaSwag: 86.8 vs 95.3 (gpt4)-8.9%
MMLU: 65.1 vs 88.3 (so35)-26.3%
TruthfulQA: 60.68 vs 59 (gpt4)2.8%
WinoGrande: 80.9 vs 87.5 (gpt4)-7.5%
GSM8K: 71.72 vs 96.4 (so35)-25.6%
LLME Score: 0.14081
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Beyonder 4x7B V2 (shadowml/Beyonder-4x7B-v2)
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Beyonder 4x7B V2 Parameters and Internals

Model Type 
Mixture of Experts (MoE), text-generation
Additional Notes 
This model uses a mixture of experts approach using Mergekit with positive prompts for specializing in specific areas.
LLM NameBeyonder 4x7B V2
Repository 🤗https://huggingface.co/shadowml/Beyonder-4x7B-v2 
Model Size24.2b
Required VRAM48.3 GB
Updated2025-08-18
Maintainershadowml
Model Typemixtral
Model Files  9.9 GB: 1-of-5   10.0 GB: 2-of-5   10.0 GB: 3-of-5   10.0 GB: 4-of-5   8.4 GB: 5-of-5
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typebfloat16

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Proto Athena V0.2 4x7B32K / 48.4 GB50
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CognitiveFusion2 4x7B BF1632K / 48.3 GB99823
Note: green Score (e.g. "73.2") means that the model is better than shadowml/Beyonder-4x7B-v2.

Rank the Beyonder 4x7B V2 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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