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Phi 2 GGUF by TheBloke

 »  All LLMs  »  TheBloke  »  Phi 2 GGUF   URL Share it on

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  Base model:microsoft/phi-2 Base model:quantized:microsoft...   Code   En   Gguf   Phi-msft   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/TheBloke/phi-2-GGUF 

Phi 2 GGUF Benchmarks

LLME Score: 0.22148
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 2 GGUF (TheBloke/phi-2-GGUF)

Phi 2 GGUF Parameters and Internals

Model Type 
phi-msft, text-generation
Use Cases 
Areas:
research
Limitations:
Generate Inaccurate Code and Facts, Unreliable Responses to Instruction, Language Limitations, Potential Societal Biases, Toxicity, Verbosity
Additional Notes 
Phi-2 is intended for research purposes only and has not been fine-tuned through reinforcement learning from human feedback.
Supported Languages 
en (standard)
Training Details 
Data Sources:
NLP synthetic data created by AOAI GPT-3.5, filtered web data from Falcon RefinedWeb and SlimPajama
Data Volume:
250B tokens
Context Length:
2048
Training Time:
14 days
Hardware Used:
96xA100-80G
Model Architecture:
Transformer-based model with next-word prediction objective
Input Output 
Input Format:
Instruct: {prompt}\nOutput:
Accepted Modalities:
text
Output Format:
text
LLM NamePhi 2 GGUF
Repository 🤗https://huggingface.co/TheBloke/phi-2-GGUF 
Model NamePhi 2
Model CreatorMicrosoft
Base Model(s)  Phi 2   microsoft/phi-2
Required VRAM1.2 GB
Updated2024-12-22
MaintainerTheBloke
Model Typephi-msft
Model Files  1.2 GB   1.6 GB   1.5 GB   1.2 GB   1.6 GB   1.8 GB   1.6 GB   1.9 GB   2.1 GB   1.9 GB   2.3 GB   3.0 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licenseother

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

Rank the Phi 2 GGUF 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 v20241217
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