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Llama 3.1 SauerkrautLM 8B Instruct by VAGOsolutions

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Model Card on HF 🤗: https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct 

Llama 3.1 SauerkrautLM 8B Instruct Benchmarks

MMLU Pro: 32.12
GPQA: 5.37
MUSR: 11.52
BBH: 31
IFEval: 80.17 vs 88 (so35)-8.9%
MATH Lvl 5: 19.41
LLME Score: 0.3611
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3.1 SauerkrautLM 8B Instruct (VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct)

Llama 3.1 SauerkrautLM 8B Instruct Parameters and Internals

Model Type 
fine-tuned
Additional Notes 
Spectrum Fine-Tuning can efficiently enhance a large language model's capabilities in multiple languages while preserving the majority of its previously acquired knowledge.
Supported Languages 
German (high), English (high), Italian (basic), French (basic), Portuguese (basic), Spanish (basic)
Training Details 
Data Sources:
Unique German-English Sauerkraut Mix v2
Methodology:
Spectrum Fine-Tuning targeting 25% of the layers
LLM NameLlama 3.1 SauerkrautLM 8B Instruct
Repository 🤗https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct 
Model Size8b
Required VRAM16 GB
Updated2025-02-22
MaintainerVAGOsolutions
Model Typellama
Instruction-BasedYes
Model Files  4.0 GB: 1-of-5   3.9 GB: 2-of-5   3.9 GB: 3-of-5   3.9 GB: 4-of-5   0.3 GB: 5-of-5
Supported Languagesde en it fr pt es
Model ArchitectureLlamaForCausalLM
Licensellama3.1
Context Length131072
Model Max Length131072
Transformers Version4.44.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
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Release v20241227
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