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Beyonder 4x7B V2 by shadowml

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Beyonder 4x7B V2 is an open-source language model by shadowml. Features: 24.2b LLM, VRAM: 48.3GB, Context: 32K, License: apache-2.0, MoE, HF Score: 72.3, LLM Explorer Score: 0.12, Arc: 68.8, HellaSwag: 86.8, MMLU: 65.1, TruthfulQA: 60.7, WinoGrande: 80.9, GSM8K: 71.7.

  Endpoints compatible   Merge   Mergekit   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
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: 0.12117
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
Updated2026-02-01
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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Calme 4x7B MoE V0.232K / 48.3 GB78902
Calme 4x7B MoE V0.132K / 48.3 GB80032
MixtureofMerges MoE 4x7b V532K / 48.3 GB81171
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CognitiveFusion2 4x7B BF1632K / 48.3 GB84923
Proto Athena 4x7B32K / 48.4 GB50
Proto Athena V0.2 4x7B32K / 48.4 GB60
Note: green Score (e.g. "73.2") means that the model is better than shadowml/Beyonder-4x7B-v2.

Rank the Beyonder 4x7B V2 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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