Phi 2 by microsoft

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  Autotrain compatible   Code   En   Endpoints compatible   Phi   Region:us   Safetensors   Sharded   Tensorflow

Phi 2 Benchmarks

Phi 2 Parameters and Internals

Model Type 
text generation, code generation
Use Cases 
Areas:
research
Applications:
QA, chat, code format
Primary Use Cases:
QA format, chat format, code format
Limitations:
Generate Inaccurate Code and Facts, Limited Scope for code, Unreliable Responses to Instruction, Language Limitations, Potential Societal Biases, Toxicity, Verbosity
Additional Notes 
Phi-2 is intended for QA, chat, and code purposes. Model-generated text/code should be treated as a starting point. Users should be cautious when employing these models in applications.
Supported Languages 
en (Standard English)
Training Details 
Data Sources:
Phi-1.5, NLP synthetic texts, filtered websites
Data Volume:
250B tokens
Methodology:
Transformer-based model with next-word prediction objective
Context Length:
2048
Training Time:
14 days
Hardware Used:
96xA100-80G GPUs
Model Architecture:
Transformer-based model with next-word prediction objective
Input Output 
Accepted Modalities:
text
Performance Tips:
Phi-2 has an attention overflow issue (with FP16). If encountering this issue, enable/disable autocast on the PhiAttention.forward() function.
LLM NamePhi 2
Repository ๐Ÿค—https://huggingface.co/microsoft/phi-2 
Model Size2.8b
Required VRAM5.6 GB
Updated2024-11-21
Maintainermicrosoft
Model Typephi
Model Files  5.0 GB: 1-of-2   0.6 GB: 2-of-2
Supported Languagesen
Model ArchitecturePhiForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.37.0
Tokenizer ClassCodeGenTokenizer
Vocabulary Size51200
Torch Data Typefloat16
Phi 2 (microsoft/phi-2)

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Rank the Phi 2 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