Vit Large Patch32 384 Breast Histopathology Images by DunnBC22

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Vit Large Patch32 384 Breast Histopathology Images is an open-source language model by DunnBC22. Features: LLM, License: apache-2.0, LLM Explorer Score: 0.08.

  Adapter   Dataset:imagefolder   En   Finetuned   Generated from trainer   Image-classification   Lora   Medical   Region:us   Tensorboard

Vit Large Patch32 384 Breast Histopathology Images Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vit Large Patch32 384 Breast Histopathology Images (DunnBC22/vit-large-patch32-384-Breast_Histopathology_Images)
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Vit Large Patch32 384 Breast Histopathology Images Parameters and Internals

Model Type 
image-classification
Use Cases 
Primary Use Cases:
Medical image classification
Limitations:
The model's intended to demonstrate the creator's ability to solve a complex problem using technology.
Supported Languages 
en (Native)
Training Details 
Data Sources:
https://huggingface.co/datasets/EulerianKnight/breast-histopathology-images-train-test-valid-split
Methodology:
Fine-tuning
Model Architecture:
ViT (Vision Transformer)
LLM NameVit Large Patch32 384 Breast Histopathology Images
Repository ๐Ÿค—https://huggingface.co/DunnBC22/vit-large-patch32-384-Breast_Histopathology_Images 
Required VRAM0 GB
Updated2026-04-08
MaintainerDunnBC22
Model Files  0.0 GB   0.0 GB
Supported Languagesen
Model ArchitectureAdapter
Licenseapache-2.0
Is Biasednone
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesquery|value
LoRA Alpha16
LoRA Dropout0.1
R Param16

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Note: green Score (e.g. "73.2") means that the model is better than DunnBC22/vit-large-patch32-384-Breast_Histopathology_Images.

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
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Code Generation  
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Release v20260328a