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Winterreise M7 by Sao10K

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  Autotrain compatible   Dataset:chargoddard/rpguild   Dataset:ldjnr/capybara   Dataset:lemonilia/limarp Dataset:pocketdoc/guanaco-unch...   En   Endpoints compatible   License:cc-by-nc-4.0   Mistral   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/Sao10K/Winterreise-m7 

Winterreise M7 Benchmarks

ARC: 61.26 vs 96.7 (so35)-36.6%
HellaSwag: 83.84 vs 95.3 (gpt4)-12%
MMLU: 63.85 vs 88.3 (so35)-27.7%
TruthfulQA: 45.55 vs 59 (gpt4)-22.8%
WinoGrande: 79.08 vs 87.5 (gpt4)-9.6%
GSM8K: 37.6 vs 96.4 (so35)-61%
LLME Score: 0.26832
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Winterreise M7 (Sao10K/Winterreise-m7)

Winterreise M7 Parameters and Internals

LLM NameWinterreise M7
Repository 🤗https://huggingface.co/Sao10K/Winterreise-m7 
Required VRAM14.4 GB
Updated2024-07-04
MaintainerSao10K
Model Typemistral
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licensecc-by-nc-4.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Sao10K/Winterreise-m7.

Rank the Winterreise M7 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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Release v20241227
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