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NebulaNet V2 4x7B MoE by davideuler

 »  All LLMs  »  davideuler  »  NebulaNet V2 4x7B MoE   URL Share it on

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  Autotrain compatible Base model:contextualai/contex... Base model:quantized:contextua...   Coding   Conversational   Endpoints compatible   Gguf   Mixtral   Moe   Multilingual   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/davideuler/NebulaNet-v2-4x7B-moe 

NebulaNet V2 4x7B MoE Benchmarks

LLME Score: 0.17987
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
NebulaNet V2 4x7B MoE (davideuler/NebulaNet-v2-4x7B-moe)

NebulaNet V2 4x7B MoE Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
coding, multi language translation, chat, math
Additional Notes 
NebulaNet-v2 is an MOE (Mixture of Experts) model consisting of 4 expert models, each with 7 billion parameters.
Supported Languages 
multilingual ()
LLM NameNebulaNet V2 4x7B MoE
Repository 🤗https://huggingface.co/davideuler/NebulaNet-v2-4x7B-moe 
Base Model(s)  Contextual KTO Mistral PairRM   ContextualAI/Contextual_KTO_Mistral_PairRM
Required VRAM8.8 GB
Updated2025-02-05
Maintainerdavideuler
Model Typemixtral
Model Files  8.8 GB   19.8 GB   25.7 GB
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureMixtralForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.39.1
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than davideuler/NebulaNet-v2-4x7B-moe.

Rank the NebulaNet V2 4x7B MoE 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 v20241227
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