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Bepis 9B by ChaoticNeutrals

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Bepis 9B is an open-source language model by ChaoticNeutrals. Features: 9b LLM, VRAM: 18GB, Context: 32K, License: other, Merged, HF Score: 62.4, LLM Explorer Score: 0.14, Arc: 62.5, HellaSwag: 80.1, MMLU: 62.8, TruthfulQA: 53.3, WinoGrande: 76.5, GSM8K: 39.1.

  Merged Model   En   Endpoints compatible   Mistral   Model-index   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/ChaoticNeutrals/Bepis_9B 

Bepis 9B Benchmarks

ARC: 62.54 vs 96.7 (so35)-35.3%
HellaSwag: 80.12 vs 95.3 (gpt4)-15.9%
MMLU: 62.84 vs 88.3 (so35)-28.8%
TruthfulQA: 53.3 vs 59 (gpt4)-9.7%
WinoGrande: 76.48 vs 87.5 (gpt4)-12.6%
GSM8K: 39.12 vs 96.4 (so35)-59.4%
LLME Score: 0.14079
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Bepis 9B (ChaoticNeutrals/Bepis_9B)
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Bepis 9B Parameters and Internals

Model Type 
text-generation
Additional Notes 
Bepis_9B is a new 9B model from jeiku, proficient in markdown, with a soulful touch. Its unique merge consists of a balance between three base models: Prodigy_7B, Prima-LelantaclesV6-7b, and Thespis-CurtainCall-7b-v0.2.1.
Training Details 
Data Sources:
AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, Winogrande, GSM8k
Methodology:
Slices method with sources and layer ranges for merging models.
Model Architecture:
Merge of three models using slices and passthrough methodology.
LLM NameBepis 9B
Repository 🤗https://huggingface.co/ChaoticNeutrals/Bepis_9B 
Merged ModelYes
Model Size9b
Required VRAM18 GB
Updated2026-02-25
MaintainerChaoticNeutrals
Model Typemistral
Model Files  10.0 GB: 1-of-2   8.0 GB: 2-of-2
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.38.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than ChaoticNeutrals/Bepis_9B.

Rank the Bepis 9B 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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