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Vangelus Secundus by ClaudioItaly

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Vangelus Secundus is an open-source language model by ClaudioItaly. Features: 9b LLM, VRAM: 18.6GB, Context: 8K, Merged, LLM Explorer Score: 0.17.

  Merged Model   Arxiv:2306.01708 Base model:claudioitaly/1800-9... Base model:ifable/gemma-2-ifab... Base model:sam-paech/delirium-...   Conversational   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/ClaudioItaly/Vangelus-Secundus 

Vangelus Secundus Benchmarks

LLME Score: 0.17099
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vangelus Secundus (ClaudioItaly/Vangelus-Secundus)
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Vangelus Secundus Parameters and Internals

LLM NameVangelus Secundus
Repository 🤗https://huggingface.co/ClaudioItaly/Vangelus-Secundus 
Base Model(s)  1800 9B   Gemma 2 Ifable 9B   Delirium V1   ClaudioItaly/1800-9B   ifable/gemma-2-Ifable-9B   sam-paech/Delirium-v1
Merged ModelYes
Model Size9b
Required VRAM18.6 GB
Updated2026-05-16
MaintainerClaudioItaly
Model Typegemma2
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   3.6 GB: 4-of-4
Model ArchitectureGemma2ForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.46.2
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than ClaudioItaly/Vangelus-Secundus.

Rank the Vangelus Secundus 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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