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Empower Functions Medium by empower-dev

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  Autotrain compatible   Conversational   Endpoints compatible   Function   Function-calling   Instruct   Mixtral   Moe   Pytorch   Region:us   Sharded   Tool-using

Empower Functions Medium Benchmarks

LLME Score: 0.22028
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Empower Functions Medium Parameters and Internals

LLM NameEmpower Functions Medium
Repository 🤗https://huggingface.co/empower-dev/empower-functions-medium 
Required VRAM93.6 GB
Updated2024-09-07
Maintainerempower-dev
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
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.38.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typebfloat16
Empower Functions Medium (empower-dev/empower-functions-medium)

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Note: green Score (e.g. "73.2") means that the model is better than empower-dev/empower-functions-medium.

Rank the Empower Functions Medium 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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