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Umbra MoE 4x10.7 4.0bpw H6 EXL2 by LoneStriker

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Model Card on HF 🤗: https://huggingface.co/LoneStriker/Umbra-MoE-4x10.7-4.0bpw-h6-exl2 

Umbra MoE 4x10.7 4.0bpw H6 EXL2 Benchmarks

LLME Score: 0.13304
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Umbra MoE 4x10.7 4.0bpw H6 EXL2 (LoneStriker/Umbra-MoE-4x10.7-4.0bpw-h6-exl2)
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Umbra MoE 4x10.7 4.0bpw H6 EXL2 Parameters and Internals

Model Type 
General Assistant, Storytelling, RP/ERP
Use Cases 
Areas:
General Knowledge, RP/ERP
Additional Notes 
Built for general assistance and storytelling capabilities. Coherence extends up to 16,000 context length.
Training Details 
Methodology:
Mixure of Experts (MoE)
Context Length:
16000
Model Architecture:
Combination MoE of Solar models
Input Output 
Input Format:
ChatML
LLM NameUmbra MoE 4x10.7 4.0bpw H6 EXL2
Repository 🤗https://huggingface.co/LoneStriker/Umbra-MoE-4x10.7-4.0bpw-h6-exl2 
Model Size10.7b
Required VRAM18.3 GB
Updated2025-09-22
MaintainerLoneStriker
Model Typemixtral
Instruction-BasedYes
Model Files  8.6 GB: 1-of-3   8.6 GB: 2-of-3   1.1 GB: 3-of-3
Quantization Typeexl2
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/Umbra-MoE-4x10.7-4.0bpw-h6-exl2.

Rank the Umbra MoE 4x10.7 4.0bpw H6 EXL2 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
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