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Phi 2 Pytdml by microsoft

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  Autotrain compatible   Code   En   Endpoints compatible   Phi   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/microsoft/phi-2-pytdml 

Phi 2 Pytdml Benchmarks

LLME Score: 0.19587
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 2 Pytdml (microsoft/phi-2-pytdml)

Phi 2 Pytdml Parameters and Internals

Model Type 
Transformer, text generation
Use Cases 
Areas:
research
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 has been optimized with DirectML and the model maintains compatibility with original checkpoints though some weights need updates.
Supported Languages 
en (standard)
Training Details 
Data Sources:
NLP synthetic data created by AOAI GPT-3.5, filtered web data from Falcon RefinedWeb, SlimPajama
Data Volume:
250 billion tokens
Methodology:
next-word prediction objective
Context Length:
2048
Training Time:
14 days
Hardware Used:
96xA100-80G
Model Architecture:
Transformer-based model
LLM NamePhi 2 Pytdml
Repository 🤗https://huggingface.co/microsoft/phi-2-pytdml 
Model Size2.8b
Required VRAM5.6 GB
Updated2025-02-22
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

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Note: green Score (e.g. "73.2") means that the model is better than microsoft/phi-2-pytdml.

Rank the Phi 2 Pytdml Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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