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Phi 2 Ov Quantized by Intel

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  Autotrain compatible   Code   En   Endpoints compatible   Openvino   Phi   Region:us
Model Card on HF 🤗: https://huggingface.co/Intel/phi-2-ov-quantized 

Phi 2 Ov Quantized Benchmarks

LLME Score: 0.16524
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Phi 2 Ov Quantized Parameters and Internals

Model Type 
Transformer-based, text-generation
Use Cases 
Areas:
Research, Commercial applications
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
Considerations:
Users should treat generated content as starting points and verify all outputs, especially in unfamiliar contexts.
Supported Languages 
English (standard)
Training Details 
Data Sources:
NLP synthetic texts, filtered websites
Model Architecture:
Transformer
LLM NamePhi 2 Ov Quantized
Repository 🤗https://huggingface.co/Intel/phi-2-ov-quantized 
Required VRAM1.9 GB
Updated2025-06-09
MaintainerIntel
Model Typephi
Model Files  1.9 GB
Supported Languagesen
Model ArchitecturePhiForCausalLM
Licensemit
Context Length2048
Model Max Length2048
Transformers Version4.37.0
Tokenizer ClassCodeGenTokenizer
Vocabulary Size51200
Torch Data Typefloat16
Phi 2 Ov Quantized (Intel/phi-2-ov-quantized)

Quantized Models of the Phi 2 Ov Quantized

Model
Likes
Downloads
VRAM
Phi 2 Quantized112121 GB

Best Alternatives to Phi 2 Ov Quantized

Best Alternatives
Context / RAM
Downloads
Likes
Phi1.5 Quantized 22K /  GB150
Merlin1.42K / 5.6 GB160
Merlin1.22K / 5.6 GB170
Merlin1.52K / 5.6 GB100
Merlin1.32K / 5.6 GB130
Tinyllama Quant2K / 0.8 GB181
Phi1.5 Update 42K /  GB190
Phi 2 Fp82K / 3.3 GB890
Phi 2 Fp82K / 3.3 GB130
Phi 2 Int8 Ov2K / 0 GB420
Note: green Score (e.g. "73.2") means that the model is better than Intel/phi-2-ov-quantized.

Rank the Phi 2 Ov Quantized 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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