SauerkrautLM Mixtral 8x7B GPTQ by TheBloke

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SauerkrautLM Mixtral 8x7B GPTQ is an open-source language model by TheBloke. Features: 46.7b LLM, VRAM: 23.8GB, Context: 32K, License: apache-2.0, MoE, Quantized, LLM Explorer Score: 0.11.

  4-bit   Augmentation Base model:quantized:vagosolut... Base model:vagosolutions/sauer...   Chatml Dataset:argilla/distilabel-mat...   Dataset:open-orca/slimorca   De   Dpo   En   Es   Finetuned   Fr   German   Gptq   It   Mistral   Mixtral   Moe   Quantized   Region:us   Safetensors   Sft

SauerkrautLM Mixtral 8x7B GPTQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

SauerkrautLM Mixtral 8x7B GPTQ Parameters and Internals

LLM NameSauerkrautLM Mixtral 8x7B GPTQ
Repository 🤗https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-GPTQ 
Model NameSauerkrautLM Mixtral 8X7B
Model CreatorVAGO solutions
Base Model(s)  SauerkrautLM Mixtral 8x7B   VAGOsolutions/SauerkrautLM-Mixtral-8x7B
Model Size46.7b
Required VRAM23.8 GB
Updated2026-05-17
MaintainerTheBloke
Model Typemixtral
Model Files  23.8 GB
Supported Languagesen de fr it es
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32002
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/SauerkrautLM-Mixtral-8x7B-GPTQ.

Rank the SauerkrautLM Mixtral 8x7B GPTQ Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
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Data Analysis and Insight Generation  
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Release v20260328a