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SauerkrautLM Gemma 7B by VAGOsolutions

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SauerkrautLM Gemma 7B Benchmarks

MMLU Pro: 21.79
GPQA: 4.81
MUSR: 2.93
BBH: 18.49
IFEval: 34.07 vs 88 (so35)-61.3%
ARC: 59.98 vs 96.7 (so35)-38%
HellaSwag: 81.91 vs 95.3 (gpt4)-14.1%
MMLU: 63.76 vs 88.3 (so35)-27.8%
TruthfulQA: 61 vs 59 (gpt4)3.4%
WinoGrande: 76.64 vs 87.5 (gpt4)-12.4%
GSM8K: 63.68 vs 96.4 (so35)-33.9%
MATH Lvl 5: 5.51
LLME Score: 0.2836
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

SauerkrautLM Gemma 7B Parameters and Internals

Model Type 
finetuned
Additional Notes 
Early stage finetuned model with potential strange behavior.
Supported Languages 
de (trained), en (trained)
Training Details 
Data Sources:
SFT, DPO, Laser data(x), Laser again on data(y)
Methodology:
Spherical Linear Interpolation and a lasered version of the model, partially freezing with laser-like analysis
Model Architecture:
novel training technique: laser-QLoRA
Input Output 
Input Format:
vicuna prompt template
Performance Tips:
Use stopping strings "~~", " "
Release Notes 
Version:
01.03.2024
Date:
2024-03-01
Notes:
Reuploaded the model in bfloat16 dtype.
Version:
02.03.2024
Date:
2024-03-02
Notes:
Strongest Gemma finetune model so far with additional scoring.
LLM NameSauerkrautLM Gemma 7B
Repository 🤗https://huggingface.co/VAGOsolutions/SauerkrautLM-Gemma-7b 
Model Size7b
Required VRAM17.1 GB
Updated2024-11-21
MaintainerVAGOsolutions
Model Typegemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.1 GB: 4-of-4
Supported Languagesde en
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.39.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16
SauerkrautLM Gemma 7B (VAGOsolutions/SauerkrautLM-Gemma-7b)

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Note: green Score (e.g. "73.2") means that the model is better than VAGOsolutions/SauerkrautLM-Gemma-7b.

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Instruction Following and Task Automation  
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Release v20241110
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