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SauerkrautLM Mixtral 8x7B Instruct by VAGOsolutions

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  Augmentation   Autotrain compatible   Conversational Dataset:argilla/distilabel-mat...   De   Dpo   En   Endpoints compatible   Es   Finetuned   Fr   German   Instruct   It   Mistral   Mixtral   Moe   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct 

SauerkrautLM Mixtral 8x7B Instruct Benchmarks

MMLU Pro: 29.45
GPQA: 6.38
MUSR: 11.32
BBH: 33.95
IFEval: 56.02 vs 88 (so35)-36.3%
ARC: 70.56 vs 96.7 (so35)-27%
HellaSwag: 87.74 vs 95.3 (gpt4)-7.9%
MMLU: 71.08 vs 88.3 (so35)-19.5%
TruthfulQA: 65.72 vs 59 (gpt4)11.4%
WinoGrande: 81.45 vs 87.5 (gpt4)-6.9%
GSM8K: 59.82 vs 96.4 (so35)-37.9%
MATH Lvl 5: 9.82
LLME Score: 0.22034
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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SauerkrautLM Mixtral 8x7B Instruct Parameters and Internals

Model Type 
Mixture of Experts (MoE), text-generation
Use Cases 
Areas:
research, commercial applications
Applications:
language understanding and generation, multilingual processing
Primary Use Cases:
text generation, instruction following
Considerations:
Ensure checking for uncensored content and maintain usage to comply with applicable laws and ethics.
Additional Notes 
Model is aligned with new German SauerkrautLM-DPO dataset, maintaining high linguistic accuracy in multiple languages, especially German.
Supported Languages 
English (fluent), German (fluent), French (fluent), Italian (fluent), Spanish (fluent)
Training Details 
Data Sources:
argilla/distilabel-math-preference-dpo, HuggingFaceH4/ultrafeedback_binarized, SFT SauerkrautLM dataset
Methodology:
DPO for alignment and German data augmentation
Model Architecture:
Mixture of Experts (MoE)
Input Output 
Input Format:
Utilizes an instruct-based prompt format with initial and follow-up instructions.
Accepted Modalities:
text
Output Format:
Text outputs as per instructions provided.
Performance Tips:
Ensure that input prompts are clear and consistent to enhance response quality.
LLM NameSauerkrautLM Mixtral 8x7B Instruct
Repository 🤗https://huggingface.co/VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct 
Model Size46.7b
Required VRAM93.6 GB
Updated2025-06-09
MaintainerVAGOsolutions
Model Typemixtral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-19   5.0 GB: 2-of-19   5.0 GB: 3-of-19   4.9 GB: 4-of-19   5.0 GB: 5-of-19   5.0 GB: 6-of-19   4.9 GB: 7-of-19   5.0 GB: 8-of-19   5.0 GB: 9-of-19   4.9 GB: 10-of-19   5.0 GB: 11-of-19   5.0 GB: 12-of-19   5.0 GB: 13-of-19   4.9 GB: 14-of-19   5.0 GB: 15-of-19   5.0 GB: 16-of-19   4.9 GB: 17-of-19   5.0 GB: 18-of-19   4.2 GB: 19-of-19
Supported Languagesen de fr it es
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.0.dev0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16
SauerkrautLM Mixtral 8x7B Instruct (VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct)

Quantized Models of the SauerkrautLM Mixtral 8x7B Instruct

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VRAM
...tLM Mixtral 8x7B Instruct GPTQ353723 GB
...tLM Mixtral 8x7B Instruct GGUF1124015 GB
...utLM Mixtral 8x7B Instruct AWQ21224 GB

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Mixtral 8x7B Instruct V0.132K / 93.6 GB2695674452
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Mixtral 8x7B Instruct V0.1 FP832K / 47.1 GB3740
Merge Mixtral Prometheus 8x7B32K / 91.9 GB192
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Karakuri Lm 8x7b Instruct V0.132K / 93.6 GB133219
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Note: green Score (e.g. "73.2") means that the model is better than VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct.

Rank the SauerkrautLM Mixtral 8x7B Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20241124
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