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SauerkrautLM Nemo 12B Instruct by VAGOsolutions

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Model Card on HF 🤗: https://huggingface.co/VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct 

SauerkrautLM Nemo 12B Instruct Benchmarks

MMLU Pro: 26.5
GPQA: 7.94
MUSR: 17.16
BBH: 32.34
IFEval: 61.13 vs 88 (so35)-30.5%
MATH Lvl 5: 9.52
LLME Score: 0.34855
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
SauerkrautLM Nemo 12B Instruct (VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct)

SauerkrautLM Nemo 12B Instruct Parameters and Internals

Model Type 
Fine-tuned, Large Language Model
Supported Languages 
German (Advanced), English (Advanced), Italian (Basic), French (Basic), Portuguese (Basic), Dutch (Basic), Russian (Basic), Arabic (Basic), Spanish (Basic)
Training Details 
Data Sources:
Unique German-English Sauerkraut Mix v2
Data Volume:
Premium Dataset
Methodology:
Spectrum Fine-Tuning targeting 25% of the layers
LLM NameSauerkrautLM Nemo 12B Instruct
Repository 🤗https://huggingface.co/VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct 
Model Size12b
Required VRAM24.5 GB
Updated2025-02-05
MaintainerVAGOsolutions
Model Typemistral
Instruction-BasedYes
Model Files  6.0 GB: 1-of-5   6.0 GB: 2-of-5   6.0 GB: 3-of-5   6.0 GB: 4-of-5   0.5 GB: 5-of-5
Supported Languagesde en it fr pt nl ru ar es
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length1024000
Model Max Length1024000
Transformers Version4.43.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size131072
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct.

Rank the SauerkrautLM Nemo 12B Instruct 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  
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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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