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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: 0.18461
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 2 Ov Quantized (Intel/phi-2-ov-quantized)

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
Updated2025-02-10
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

Quantized Models of the Phi 2 Ov Quantized

Model
Likes
Downloads
VRAM
Phi 2 Quantized111591 GB

Best Alternatives to Phi 2 Ov Quantized

Best Alternatives
Context / RAM
Downloads
Likes
Phi1.5 Quantized 22K /  GB50
Merlin1.42K / 5.6 GB1010
Merlin1.52K / 5.6 GB1020
Merlin1.22K / 5.6 GB880
Merlin1.32K / 5.6 GB930
Phi Bode 2 Ultraalpaca2K / 5.6 GB4522
Tinyllama Quant2K / 0.8 GB41
Phi1.5 Update 42K /  GB60
Phi 2 Fp82K / 3.3 GB890
Phi 2 Fp82K / 3.3 GB810
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 v20241227
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