model_name, use_tracing, dynamic, param_count, tags, notes
microsoft/MiniLM-L12-H384-uncased,True,True,66M,"nlp;bert-variant;transformer-encoder","Large version has 12 layers; 384 hidden size; Smaller than BERTbase (66M params vs 109M params)"
albert-base-v2,True,True,11M,"nlp;bert-variant;transformer-encoder","12 layers; 128 embedding dim; 768 hidden dim; 12 attention heads; Smaller than BERTbase (11M params vs 109M params); Uses weight sharing to reduce # params but computational cost is similar to BERT."
bert-base-uncased,True,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
bert-base-uncased_fp16,True,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
bert-base-cased,True,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
distilbert-base-uncased,True,True,66M,"nlp;bert-variant;transformer-encoder","Smaller and faster than BERT with 97percent retained accuracy."
google/mobilebert-uncased,True,True,25M,"nlp,bert-variant,transformer-encoder,mobile","24 layers, 512 hidden size, 128 embedding"
alexnet,False,True,61M,"cnn,parallel-layers","The CNN that revolutionized computer vision (move away from hand-crafted features to neural networks),10 years old now and probably no longer used in prod."
resnet18,False,True,11M,"cnn,image-classification,residuals,resnet-variant","1 7x7 conv2d and the rest are 3x3 conv2d"
resnet50,False,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
resnet50_fp16,False,True,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
resnet101,False,True,29M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
squeezenet1_0,False,True,1.25M,"cnn,image-classification,mobile,parallel-layers","Parallel conv2d (1x1 conv to compress -> (3x3 expand | 1x1 expand) -> concat)"
wide_resnet50_2,False,True,69M,"cnn,image-classification,residuals,resnet-variant","Resnet variant where model depth is decreased and width is increased."
mobilenet_v3_small,False,True,2.5M,"image-classification,cnn,mobile",N/A
google/vit-base-patch16-224,True,False,86M,"image-classification,vision-transformer,transformer-encoder",N/A
microsoft/resnet-50,True,False,23M,"image-classification,cnn,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
facebook/deit-small-distilled-patch16-224,True,False,22M,"image-classification,vision-transformer,cnn",N/A
microsoft/beit-base-patch16-224-pt22k-ft22k,True,False,86M,"image-classification,transformer-encoder,bert-variant,vision-transformer",N/A
nvidia/mit-b0,True,False,3.7M,"image-classification,transformer-encoder",SegFormer
camembert-base,False,False,-,-,-
dbmdz/convbert-base-turkish-cased,False,False,-,-,-
google/electra-small-discriminator,False,False,-,-,-
hf-internal-testing/tiny-random-flaubert,False,False,-,-,-
funnel-transformer/small,False,False,-,-,-
microsoft/layoutlm-base-uncased,False,False,-,-,-
microsoft/mpnet-base,False,False,-,-,-
roberta-base,False,False,-,-,-
xlm-roberta-base,False,False,-,-,-
facebook/convnext-tiny-224,False,False,-,-,-
efficientnet-v2-s,False,False,22M,"image-classification,cnn","Includes MBConv and Fused-MBConv"
mnasnet1_0,False,True,-,"cnn, torchvision, mobile, architecture-search","Outperforms other mobile CNNs on Accuracy vs. Latency"
bert-large-uncased,True,True,330M,"nlp;bert-variant;transformer-encoder","24 layers, 1024 hidden units, 16 attention heads"
t5-base,True,False,220M,"nlp;transformer-encoder;transformer-decoder","Text-to-Text Transfer Transformer"
t5-large,True,False,770M,"nlp;transformer-encoder;transformer-decoder","Text-to-Text Transfer Transformer"
bert-large-uncased,True,hf,True,330M,"nlp;bert-variant;transformer-encoder","24 layers, 1024 hidden units, 16 attention heads"
efficientnet_b0,True,False,5.3M,"image-classification;cnn;conv2d;depthwise-conv","Smallest EfficientNet variant with 224x224 input"
efficientnet_b7,True,False,66M,"image-classification;cnn;conv2d;depthwise-conv","Largest EfficientNet variant with 600x600 input"
gpt2,True,False,110M,"nlp;transformer-decoder;auto-regressive","12 layers, 768 hidden units, 12 attention heads"
t5-base,True,False,220M,"nlp;transformer-encoder;transformer-decoder","Text-to-Text Transfer Transformer"
t5-large,True,False,770M,"nlp;transformer-encoder;transformer-decoder","Text-to-Text Transfer Transformer"