Phi 3 Vision Win Snap by Kukedlc

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Phi 3 Vision Win Snap Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phi 3 Vision Win Snap (Kukedlc/Phi-3-Vision-Win-snap)

Phi 3 Vision Win Snap Parameters and Internals

Model Type 
multimodal, text generation
Additional Notes 
The model underwent a rigorous enhancement process to ensure precise instruction adherence and robust safety measures.
Supported Languages 
multilingual (proficiency not specified)
Training Details 
Data Sources:
synthetic data, filtered publicly available websites
Methodology:
supervised fine-tuning and direct preference optimization
Context Length:
128000
LLM NamePhi 3 Vision Win Snap
Repository ๐Ÿค—https://huggingface.co/Kukedlc/Phi-3-Vision-Win-snap 
Model Size4.1b
Required VRAM8.3 GB
Updated2024-12-22
MaintainerKukedlc
Model Typephi3_v
Instruction-BasedYes
Model Files  4.9 GB: 1-of-2   3.4 GB: 2-of-2
Model ArchitecturePhi3VForCausalLM
Licensemit
Context Length131072
Model Max Length131072
Transformers Version4.38.1
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32064
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Kukedlc/Phi-3-Vision-Win-snap.

Rank the Phi 3 Vision Win Snap Capabilities

๐Ÿ†˜ 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! ๐ŸŒŸ

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 v20241217