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SauerkrautLM Mixtral 8x7B Instruct by VAGOsolutions

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SauerkrautLM Mixtral 8x7B Instruct Benchmarks

ARC: 70.56-26.7%
HellaSwag: 87.74-7.9%
MMLU: 71.08-17.8%
TruthfulQA: 65.7211.4%
WinoGrande: 81.45-6.9%
GSM8K: 59.82-35%
nn.n% — How the model compares to the GPT-4.

SauerkrautLM Mixtral 8x7B Instruct Parameters and Internals

LLM NameSauerkrautLM Mixtral 8x7B Instruct
RepositoryOpen on 🤗 
Model Size46.7b
Required VRAM93.6 GB
Updated2024-07-27
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

Model
Likes
Downloads
VRAM
...utLM Mixtral 8x7B Instruct AWQ2156524 GB
...tLM Mixtral 8x7B Instruct GPTQ349023 GB
...tLM Mixtral 8x7B Instruct GGUF934415 GB

Best Alternatives to SauerkrautLM Mixtral 8x7B Instruct

Best Alternatives
HF Rank
Context/RAM
Downloads
Likes
Mixtral 8x7B Instruct V0.10.632K / 93.6 GB6512363978
Dolphin 2.5 Mixtral 8x7b0.432K / 93.6 GB563121182
Notux 8x7b V10.332K / 93.6 GB21164
Karakuri Lm 8x7b Instruct V0.10.332K / 93.6 GB111416
XLAM V0.1 R0.332K / 93.6 GB58641
Firefunction V10.332K / 93.6 GB806121
Autotrain Xva0j Mixtral8x7b0.332K / 93.6 GB3050
Mixtral 8x7B Instruct V0.1 FP80.332K / 47.1 GB6982
Autotrain Mixtral 8x7b Orpo V20.332K / 93.6 GB2420
Autotrain Mixtral 8x7b Orpo V10.332K / 93.6 GB2500
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

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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  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v2024072501
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