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Borea Phi 3.5 Mini Instruct Jp by HODACHI

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  Autotrain compatible   Conversational   Custom code   Endpoints compatible   Instruct   Phi3   Region:us   Safetensors   Sharded   Tensorflow

Borea Phi 3.5 Mini Instruct Jp Benchmarks

LLME Score: 0.24403
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Borea Phi 3.5 Mini Instruct Jp Parameters and Internals

Model Type 
text generation, conversational
Use Cases 
Areas:
research
Additional Notes 
This model is focused on Japanese language proficiency but can be used globally. It employs several tuning methods for enhanced performance over its base model.
Supported Languages 
Japanese (high proficiency)
Training Details 
Data Sources:
Japanese Wikipedia, FineWeb
Methodology:
Instruction tuning
Hardware Used:
H100PCIe × 8 (Running in 2h)
Input Output 
Accepted Modalities:
text
LLM NameBorea Phi 3.5 Mini Instruct Jp
Repository 🤗https://huggingface.co/HODACHI/Borea-Phi-3.5-mini-Instruct-Jp 
Model Size3.8b
Required VRAM7.7 GB
Updated2024-08-23
MaintainerHODACHI
Model Typephi3
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.7 GB: 2-of-2
Model ArchitecturePhi3ForCausalLM
Licensemit
Context Length131072
Model Max Length131072
Transformers Version4.43.0
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16
Borea Phi 3.5 Mini Instruct Jp (HODACHI/Borea-Phi-3.5-mini-Instruct-Jp)

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Note: green Score (e.g. "73.2") means that the model is better than HODACHI/Borea-Phi-3.5-mini-Instruct-Jp.

Rank the Borea Phi 3.5 Mini Instruct Jp 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 v20241110
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