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WikiHow Mistral Instruct 7B by ajibawa-2023

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  Autotrain compatible   Conversational   Dataset:ajibawa-2023/wikihow   Educational   En   Endpoints compatible   Instruct   Mistral   Model-index   Pytorch   Region:us   Safetensors   Sharded   Tensorflow   Tutorial   Wikihow

WikiHow Mistral Instruct 7B Benchmarks

ARC: 60.92 vs 96.7 (so35)-37%
HellaSwag: 80.99 vs 95.3 (gpt4)-15%
MMLU: 58.57 vs 88.3 (so35)-33.7%
TruthfulQA: 62.16 vs 59 (gpt4)5.4%
WinoGrande: 74.82 vs 87.5 (gpt4)-14.5%
GSM8K: 30.02 vs 96.4 (so35)-68.9%
LLME Score: 0.23025
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

WikiHow Mistral Instruct 7B Parameters and Internals

LLM NameWikiHow Mistral Instruct 7B
Repository 🤗https://huggingface.co/ajibawa-2023/WikiHow-Mistral-Instruct-7B 
Model Size7b
Required VRAM14.4 GB
Updated2024-09-07
Maintainerajibawa-2023
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3   4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16
WikiHow Mistral Instruct 7B (ajibawa-2023/WikiHow-Mistral-Instruct-7B)

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Note: green Score (e.g. "73.2") means that the model is better than ajibawa-2023/WikiHow-Mistral-Instruct-7B.

Rank the WikiHow Mistral Instruct 7B 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 v2024072803
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