Recoilme Gemma 2 Gutenberg Doppel 9B V0.1 by zelk12

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  Merged Model   Autotrain compatible Base model:nbeerbower/gemma2-g... Base model:recoilme/recoilme-g...   Conversational   Endpoints compatible   Gemma2   Model-index   Region:us   Safetensors   Sharded   Tensorflow

Recoilme Gemma 2 Gutenberg Doppel 9B V0.1 Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Recoilme Gemma 2 Gutenberg Doppel 9B V0.1 (zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1)

Recoilme Gemma 2 Gutenberg Doppel 9B V0.1 Parameters and Internals

Model Type 
text-generation
Additional Notes 
This is a merge of pre-trained language models created using mergekit. The model was merged using the SLERP merge method with a configuration specified in the YAML.
LLM NameRecoilme Gemma 2 Gutenberg Doppel 9B V0.1
Repository ๐Ÿค—https://huggingface.co/zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1 
Base Model(s)  recoilme/recoilme-gemma-2-9B-v0.4   Gemma2 Gutenberg Doppel 9B   recoilme/recoilme-gemma-2-9B-v0.4   nbeerbower/Gemma2-Gutenberg-Doppel-9B
Merged ModelYes
Model Size9b
Required VRAM20.4 GB
Updated2025-02-22
Maintainerzelk12
Model Typegemma2
Model Files  5.0 GB: 1-of-5   5.0 GB: 2-of-5   4.9 GB: 3-of-5   5.0 GB: 4-of-5   0.5 GB: 5-of-5
Model ArchitectureGemma2ForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.45.1
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1.

Rank the Recoilme Gemma 2 Gutenberg Doppel 9B V0.1 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