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Ayam 2x8B by R136a1

 »  All LLMs  »  R136a1  »  Ayam 2x8B   URL Share it on

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  Autotrain compatible   Conversational   En   Endpoints compatible   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/R136a1/Ayam-2x8B 

Ayam 2x8B Benchmarks

LLME Score: 0.19231
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Ayam 2x8B (R136a1/Ayam-2x8B)

Ayam 2x8B Parameters and Internals

Additional Notes 
Model aims to improve intelligence while preserving creativity through Mixture of Experts (MoE) approach rather than merging models.
Supported Languages 
en (English)
Training Details 
Methodology:
MoE (Mixture of Experts)
LLM NameAyam 2x8B
Repository 🤗https://huggingface.co/R136a1/Ayam-2x8B 
Model Size13.7b
Required VRAM27.3 GB
Updated2025-02-22
MaintainerR136a1
Model Typemixtral
Model Files  5.0 GB: 1-of-6   4.9 GB: 2-of-6   5.0 GB: 3-of-6   5.0 GB: 4-of-6   4.9 GB: 5-of-6   2.5 GB: 6-of-6
Supported Languagesen
Model ArchitectureMixtralForCausalLM
Licensecc-by-nc-4.0
Context Length8192
Model Max Length8192
Transformers Version4.42.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|begin_of_text|>
Vocabulary Size128256
Torch Data Typebfloat16

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Kilo 2x8B8K / 27.5 GB121
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Inixion 2x8B V28K / 27.4 GB62
Note: green Score (e.g. "73.2") means that the model is better than R136a1/Ayam-2x8B.

Rank the Ayam 2x8B 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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