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modelId stringlengths 5 137	author stringlengths 2 42	last_modified unknowndate 2020-02-15 11:33:14 2025-03-29 12:26:52	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 401 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt unknowndate 2022-03-02 23:29:04 2025-03-29 12:26:36	card stringlengths 11 1.01M
popV/tabula_muris_All	popV	"2025-03-08T12:41:08Z"	0	0	popV	[ "popV", "joblib", "biology", "genomics", "single-cell", "anndata_version:0.11.3", "python_version:3.11.11", "tissue: diverse", "license:cc-by-4.0", "region:us" ]	null	"2025-03-08T12:40:35Z"	--- library_name: popV license: cc-by-4.0 tags: - biology - genomics - single-cell - anndata_version:0.11.3 - python_version:3.11.11 - popV - 'tissue: diverse' --- Popular Vote (popV) model for automated cell type annotation of single-cell RNA-seq data. We provide here pretrained models for plug-in use in your own analysis. Follow our [tutorial](https://github.com/YosefLab/popV/blob/main/tabula_sapiens_tutorial.ipynb) to learn how to use the model for cell type annotation. # Model description Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood. To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing—such as senescence, genomic instability and changes in the immune system. This transcriptomic atlas—which we denote Tabula Muris Senis, or ‘Mouse Ageing Cell Atlas’—provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types. Link to CELLxGENE: Link to the [data](https://cellxgene.cziscience.com/e/f16a8f4d-bc97-43c5-a2f6-bbda952e4c5c.cxg/) in the CELLxGENE browser for interactive exploration of the data and download of the source data. Training Code URL: Not provided by uploader. # Metrics We provide here accuracies for each of the experts and the ensemble model. The validation set accuracies are computed on a 10% random subset of the data that was not used for training. \| Cell Type \| N cells \| celltypist \| knn bbknn \| knn harmony \| knn on scvi \| onclass \| scanvi \| svm \| xgboost \| Consensus Prediction \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| B cell \| 3687 \| 0.86 \| 0.90 \| 0.86 \| 0.86 \| 0.00 \| 0.89 \| 0.75 \| 0.78 \| 0.91 \| \| mesenchymal stem cell \| 1678 \| 0.87 \| 0.88 \| 0.89 \| 0.68 \| 0.00 \| 0.69 \| 0.91 \| 0.87 \| 0.93 \| \| basal cell of epidermis \| 1499 \| 0.85 \| 0.88 \| 0.81 \| 0.84 \| 0.00 \| 0.84 \| 0.86 \| 0.82 \| 0.90 \| \| granulocyte \| 1556 \| 0.81 \| 0.86 \| 0.79 \| 0.74 \| 0.00 \| 0.78 \| 0.74 \| 0.80 \| 0.88 \| \| endothelial cell \| 1478 \| 0.88 \| 0.88 \| 0.82 \| 0.74 \| 0.00 \| 0.79 \| 0.90 \| 0.85 \| 0.93 \| \| microglial cell \| 1293 \| 0.99 \| 0.99 \| 0.96 \| 0.99 \| 0.00 \| 0.98 \| 0.99 \| 0.99 \| 1.00 \| \| T cell \| 1244 \| 0.75 \| 0.80 \| 0.63 \| 0.58 \| 0.00 \| 0.67 \| 0.62 \| 0.73 \| 0.82 \| \| keratinocyte \| 1254 \| 0.85 \| 0.89 \| 0.81 \| 0.80 \| 0.00 \| 0.86 \| 0.88 \| 0.85 \| 0.91 \| \| macrophage \| 971 \| 0.47 \| 0.68 \| 0.59 \| 0.51 \| 0.00 \| 0.48 \| 0.53 \| 0.56 \| 0.71 \| \| classical monocyte \| 765 \| 0.75 \| 0.60 \| 0.61 \| 0.56 \| 0.00 \| 0.78 \| 0.79 \| 0.83 \| 0.88 \| \| granulocytopoietic cell \| 807 \| 0.67 \| 0.77 \| 0.62 \| 0.51 \| 0.00 \| 0.46 \| 0.68 \| 0.75 \| 0.80 \| \| bladder urothelial cell \| 680 \| 0.98 \| 0.99 \| 0.96 \| 0.85 \| 0.00 \| 0.96 \| 1.00 \| 0.98 \| 0.99 \| \| mesenchymal stem cell of adipose tissue \| 650 \| 0.74 \| 0.84 \| 0.72 \| 0.51 \| 0.00 \| 0.54 \| 0.90 \| 0.80 \| 0.88 \| \| fibroblast of cardiac tissue \| 613 \| 0.96 \| 0.94 \| 0.80 \| 0.46 \| 0.00 \| 0.66 \| 0.95 \| 0.91 \| 0.96 \| \| endothelial cell of coronary artery \| 624 \| 0.81 \| 0.86 \| 0.63 \| 0.58 \| 0.00 \| 0.77 \| 0.89 \| 0.84 \| 0.91 \| \| skeletal muscle satellite cell \| 465 \| 0.98 \| 0.98 \| 0.95 \| 0.97 \| 0.00 \| 0.96 \| 0.97 \| 0.97 \| 0.98 \| \| proerythroblast \| 435 \| 0.52 \| 0.56 \| 0.51 \| 0.22 \| 0.00 \| 0.26 \| 0.51 \| 0.60 \| 0.64 \| \| kidney proximal convoluted tubule epithelial cell \| 445 \| 0.84 \| 0.87 \| 0.34 \| 0.82 \| 0.00 \| 0.75 \| 0.87 \| 0.88 \| 0.89 \| \| bronchial smooth muscle cell \| 454 \| 0.96 \| 0.96 \| 0.84 \| 0.91 \| 0.00 \| 0.92 \| 0.97 \| 0.94 \| 0.96 \| \| myeloid cell \| 413 \| 0.44 \| 0.60 \| 0.41 \| 0.42 \| 0.00 \| 0.25 \| 0.70 \| 0.68 \| 0.69 \| \| hepatocyte \| 377 \| 0.98 \| 0.98 \| 0.98 \| 0.97 \| 0.00 \| 0.98 \| 0.99 \| 0.98 \| 0.99 \| \| type B pancreatic cell \| 428 \| 0.97 \| 0.98 \| 0.96 \| 0.98 \| 0.00 \| 0.98 \| 0.99 \| 0.98 \| 0.99 \| \| bladder cell \| 411 \| 0.97 \| 0.95 \| 0.97 \| 0.58 \| 0.00 \| 0.84 \| 0.98 \| 0.96 \| 0.98 \| \| promonocyte \| 365 \| 0.65 \| 0.72 \| 0.66 \| 0.61 \| 0.00 \| 0.58 \| 0.74 \| 0.78 \| 0.82 \| \| DN4 thymocyte \| 425 \| 0.39 \| 0.53 \| 0.38 \| 0.31 \| 0.00 \| 0.23 \| 0.58 \| 0.63 \| 0.62 \| \| basal cell \| 384 \| 0.98 \| 0.97 \| 0.97 \| 0.92 \| 0.00 \| 0.96 \| 0.98 \| 0.96 \| 0.98 \| \| monocyte \| 350 \| 0.60 \| 0.57 \| 0.48 \| 0.31 \| 0.00 \| 0.37 \| 0.73 \| 0.76 \| 0.84 \| \| enterocyte of epithelium of large intestine \| 374 \| 0.92 \| 0.93 \| 0.84 \| 0.92 \| 0.00 \| 0.92 \| 0.94 \| 0.92 \| 0.95 \| \| thymocyte \| 370 \| 0.82 \| 0.74 \| 0.59 \| 0.82 \| 0.00 \| 0.87 \| 0.89 \| 0.92 \| 0.88 \| \| epithelial cell of proximal tubule \| 342 \| 0.84 \| 0.82 \| 0.78 \| 0.76 \| 0.00 \| 0.82 \| 0.85 \| 0.85 \| 0.85 \| \| natural killer cell \| 352 \| 0.84 \| 0.82 \| 0.68 \| 0.68 \| 0.00 \| 0.74 \| 0.74 \| 0.80 \| 0.85 \| \| naive B cell \| 341 \| 0.46 \| 0.22 \| 0.40 \| 0.36 \| 0.00 \| 0.48 \| 0.49 \| 0.56 \| 0.58 \| \| hematopoietic stem cell \| 337 \| 0.92 \| 0.93 \| 0.88 \| 0.88 \| 0.00 \| 0.88 \| 0.94 \| 0.93 \| 0.94 \| \| mature NK T cell \| 321 \| 0.57 \| 0.64 \| 0.46 \| 0.32 \| 0.00 \| 0.59 \| 0.59 \| 0.70 \| 0.71 \| \| fibroblast \| 309 \| 0.20 \| 0.25 \| 0.53 \| 0.31 \| 0.00 \| 0.32 \| 0.56 \| 0.54 \| 0.52 \| \| stromal cell \| 331 \| 0.61 \| 0.49 \| 0.86 \| 0.33 \| 0.00 \| 0.37 \| 0.85 \| 0.71 \| 0.86 \| \| keratinocyte stem cell \| 321 \| 0.95 \| 0.95 \| 0.84 \| 0.87 \| 0.00 \| 0.93 \| 0.96 \| 0.91 \| 0.96 \| \| erythroblast \| 291 \| 0.60 \| 0.69 \| 0.66 \| 0.56 \| 0.00 \| 0.54 \| 0.66 \| 0.71 \| 0.76 \| \| Kupffer cell \| 280 \| 0.98 \| 0.95 \| 0.71 \| 0.42 \| 0.00 \| 0.83 \| 0.98 \| 0.94 \| 0.97 \| \| megakaryocyte-erythroid progenitor cell \| 244 \| 0.53 \| 0.62 \| 0.45 \| 0.41 \| 0.00 \| 0.57 \| 0.55 \| 0.66 \| 0.69 \| \| pancreatic acinar cell \| 235 \| 0.95 \| 0.95 \| 0.93 \| 0.97 \| 0.00 \| 0.98 \| 0.99 \| 0.99 \| 0.98 \| \| luminal epithelial cell of mammary gland \| 300 \| 0.94 \| 0.94 \| 0.98 \| 0.87 \| 0.00 \| 0.92 \| 0.97 \| 0.93 \| 0.97 \| \| professional antigen presenting cell \| 257 \| 0.05 \| 0.02 \| 0.09 \| 0.01 \| 0.00 \| 0.01 \| 0.41 \| 0.36 \| 0.06 \| \| epidermal cell \| 219 \| 0.84 \| 0.85 \| 0.75 \| 0.84 \| 0.00 \| 0.84 \| 0.86 \| 0.86 \| 0.90 \| \| hematopoietic precursor cell \| 216 \| 0.63 \| 0.74 \| 0.39 \| 0.24 \| 0.00 \| 0.55 \| 0.71 \| 0.78 \| 0.80 \| \| oligodendrocyte \| 224 \| 0.96 \| 0.98 \| 0.97 \| 0.99 \| 0.00 \| 0.99 \| 0.99 \| 0.99 \| 0.99 \| \| fibroblast of lung \| 216 \| 0.98 \| 0.96 \| 0.94 \| 0.90 \| 0.00 \| 0.95 \| 0.98 \| 0.94 \| 0.99 \| \| CD8-positive, alpha-beta T cell \| 183 \| 0.35 \| 0.52 \| 0.28 \| 0.32 \| 0.00 \| 0.44 \| 0.48 \| 0.57 \| 0.64 \| \| epithelial cell of large intestine \| 174 \| 0.74 \| 0.79 \| 0.57 \| 0.68 \| 0.00 \| 0.73 \| 0.85 \| 0.82 \| 0.83 \| \| intestinal crypt stem cell \| 199 \| 0.84 \| 0.86 \| 0.69 \| 0.80 \| 0.00 \| 0.82 \| 0.87 \| 0.82 \| 0.87 \| \| chondrocyte \| 179 \| 0.60 \| 0.61 \| 0.78 \| 0.68 \| 0.00 \| 0.64 \| 0.85 \| 0.85 \| 0.69 \| \| immature T cell \| 181 \| 0.72 \| 0.03 \| 0.12 \| 0.61 \| 0.00 \| 0.79 \| 0.82 \| 0.88 \| 0.77 \| \| precursor B cell \| 192 \| 0.66 \| 0.88 \| 0.55 \| 0.62 \| 0.00 \| 0.66 \| 0.78 \| 0.84 \| 0.84 \| \| intermediate monocyte \| 167 \| 0.34 \| 0.12 \| 0.36 \| 0.24 \| 0.00 \| 0.63 \| 0.53 \| 0.58 \| 0.64 \| \| large intestine goblet cell \| 163 \| 0.79 \| 0.85 \| 0.79 \| 0.76 \| 0.00 \| 0.81 \| 0.91 \| 0.86 \| 0.88 \| \| CD4-positive, alpha-beta T cell \| 164 \| 0.25 \| 0.38 \| 0.22 \| 0.26 \| 0.00 \| 0.38 \| 0.33 \| 0.45 \| 0.55 \| \| smooth muscle cell \| 167 \| 0.59 \| 0.79 \| 0.66 \| 0.61 \| 0.00 \| 0.73 \| 0.84 \| 0.81 \| 0.77 \| \| leukocyte \| 156 \| 0.52 \| 0.59 \| 0.35 \| 0.18 \| 0.00 \| 0.32 \| 0.62 \| 0.68 \| 0.77 \| \| plasma cell \| 143 \| 0.48 \| 0.66 \| 0.47 \| 0.41 \| 0.00 \| 0.60 \| 0.52 \| 0.60 \| 0.71 \| \| lymphocyte \| 173 \| 0.18 \| 0.20 \| 0.31 \| 0.13 \| 0.00 \| 0.00 \| 0.23 \| 0.38 \| 0.35 \| \| naive T cell \| 146 \| 0.79 \| 0.85 \| 0.85 \| 0.82 \| 0.00 \| 0.84 \| 0.86 \| 0.86 \| 0.87 \| \| kidney loop of Henle thick ascending limb epithelial cell \| 141 \| 0.92 \| 0.91 \| 0.66 \| 0.79 \| 0.00 \| 0.42 \| 0.89 \| 0.92 \| 0.94 \| \| immature B cell \| 147 \| 0.71 \| 0.76 \| 0.57 \| 0.69 \| 0.00 \| 0.58 \| 0.52 \| 0.71 \| 0.82 \| \| alveolar macrophage \| 134 \| 0.93 \| 0.93 \| 0.90 \| 0.89 \| 0.00 \| 0.93 \| 0.95 \| 0.90 \| 0.94 \| \| endothelial cell of hepatic sinusoid \| 122 \| 0.95 \| 0.93 \| 0.89 \| 0.81 \| 0.00 \| 0.86 \| 0.98 \| 0.93 \| 0.96 \| \| lung macrophage \| 106 \| 0.87 \| 0.09 \| 0.30 \| 0.00 \| 0.00 \| 0.81 \| 0.87 \| 0.81 \| 0.94 \| \| non-classical monocyte \| 114 \| 0.60 \| 0.73 \| 0.41 \| 0.56 \| 0.00 \| 0.69 \| 0.78 \| 0.70 \| 0.75 \| \| endocardial cell \| 113 \| 0.92 \| 0.92 \| 0.88 \| 0.85 \| 0.00 \| 0.79 \| 0.92 \| 0.85 \| 0.93 \| \| double negative thymocyte \| 105 \| 0.55 \| 0.00 \| 0.31 \| 0.20 \| 0.00 \| 0.64 \| 0.72 \| 0.81 \| 0.83 \| \| late pro-B cell \| 92 \| 0.80 \| 0.82 \| 0.37 \| 0.54 \| 0.00 \| 0.59 \| 0.68 \| 0.80 \| 0.90 \| \| kidney collecting duct principal cell \| 105 \| 0.64 \| 0.86 \| 0.78 \| 0.86 \| 0.00 \| 0.84 \| 0.91 \| 0.85 \| 0.89 \| \| basal epithelial cell of tracheobronchial tree \| 87 \| 0.89 \| 0.77 \| 0.83 \| 0.49 \| 0.00 \| 0.57 \| 0.93 \| 0.86 \| 0.93 \| \| mesenchymal cell \| 91 \| 0.20 \| 0.00 \| 0.14 \| 0.11 \| 0.00 \| 0.12 \| 0.45 \| 0.49 \| 0.42 \| \| pancreatic A cell \| 81 \| 0.61 \| 0.77 \| 0.44 \| 0.83 \| 0.00 \| 0.80 \| 0.91 \| 0.89 \| 0.86 \| \| adventitial cell \| 63 \| 0.69 \| 0.62 \| 0.45 \| 0.07 \| 0.00 \| 0.23 \| 0.76 \| 0.54 \| 0.81 \| \| epithelial cell \| 52 \| 0.38 \| 0.07 \| 0.46 \| 0.38 \| 0.00 \| 0.27 \| 0.51 \| 0.49 \| 0.62 \| \| kidney distal convoluted tubule epithelial cell \| 80 \| 0.76 \| 0.86 \| 0.62 \| 0.63 \| 0.00 \| 0.86 \| 0.87 \| 0.86 \| 0.87 \| \| neutrophil \| 73 \| 0.54 \| 0.68 \| 0.46 \| 0.18 \| 0.00 \| 0.28 \| 0.66 \| 0.58 \| 0.68 \| \| pancreatic ductal cell \| 55 \| 0.98 \| 0.81 \| 0.78 \| 0.93 \| 0.00 \| 0.96 \| 0.96 \| 0.96 \| 0.98 \| \| myeloid dendritic cell \| 54 \| 0.36 \| 0.58 \| 0.40 \| 0.33 \| 0.00 \| 0.42 \| 0.69 \| 0.62 \| 0.68 \| \| astrocyte \| 70 \| 0.93 \| 0.95 \| 0.98 \| 0.98 \| 0.00 \| 0.99 \| 0.99 \| 0.96 \| 0.99 \| \| secretory cell \| 52 \| 0.21 \| 0.49 \| 0.37 \| 0.45 \| 0.00 \| 0.65 \| 0.87 \| 0.77 \| 0.70 \| \| myeloid leukocyte \| 56 \| 0.69 \| 0.41 \| 0.42 \| 0.27 \| 0.00 \| 0.31 \| 0.44 \| 0.57 \| 0.80 \| \| plasmatocyte \| 48 \| 0.55 \| 0.78 \| 0.63 \| 0.76 \| 0.00 \| 0.61 \| 0.88 \| 0.83 \| 0.87 \| \| brain pericyte \| 56 \| 0.00 \| 0.77 \| 0.56 \| 0.76 \| 0.00 \| 0.87 \| 0.97 \| 0.84 \| 0.95 \| \| neuron \| 48 \| 0.59 \| 0.62 \| 0.66 \| 0.65 \| 0.00 \| 0.72 \| 0.90 \| 0.81 \| 0.87 \| \| vein endothelial cell \| 49 \| 0.67 \| 0.81 \| 0.64 \| 0.26 \| 0.00 \| 0.74 \| 0.86 \| 0.72 \| 0.85 \| \| kidney collecting duct epithelial cell \| 49 \| 0.00 \| 0.63 \| 0.20 \| 0.56 \| 0.00 \| 0.39 \| 0.78 \| 0.85 \| 0.91 \| \| pancreatic D cell \| 46 \| 0.00 \| 0.76 \| 0.52 \| 0.89 \| 0.00 \| 0.95 \| 0.96 \| 0.96 \| 0.97 \| \| aortic endothelial cell \| 12 \| 0.08 \| 0.12 \| 0.18 \| 0.21 \| 0.00 \| 0.14 \| 0.13 \| 0.08 \| 0.37 \| \| brush cell \| 37 \| 0.78 \| 0.59 \| 0.35 \| 0.68 \| 0.00 \| 0.80 \| 0.72 \| 0.73 \| 0.81 \| \| erythroid progenitor cell, mammalian \| 48 \| 0.57 \| 0.00 \| 0.18 \| 0.05 \| 0.00 \| 0.26 \| 0.79 \| 0.65 \| 0.49 \| \| regular atrial cardiac myocyte \| 35 \| 0.00 \| 0.58 \| 0.44 \| 0.44 \| 0.00 \| 0.37 \| 0.52 \| 0.66 \| 0.77 \| \| kidney cortex artery cell \| 40 \| 0.00 \| 0.83 \| 0.58 \| 0.24 \| 0.00 \| 0.62 \| 0.83 \| 0.62 \| 0.85 \| \| erythrocyte \| 44 \| 0.63 \| 0.63 \| 0.64 \| 0.63 \| 0.00 \| 0.67 \| 0.74 \| 0.85 \| 0.80 \| \| mesangial cell \| 32 \| 0.00 \| 0.58 \| 0.22 \| 0.28 \| 0.00 \| 0.50 \| 0.66 \| 0.59 \| 0.81 \| \| kidney loop of Henle ascending limb epithelial cell \| 32 \| 0.56 \| 0.59 \| 0.43 \| 0.33 \| 0.00 \| 0.35 \| 0.72 \| 0.67 \| 0.74 \| \| cardiac muscle cell \| 32 \| 0.63 \| 0.81 \| 0.62 \| 0.71 \| 0.00 \| 0.94 \| 1.00 \| 0.97 \| 0.97 \| \| mature alpha-beta T cell \| 37 \| 0.00 \| 0.27 \| 0.28 \| 0.38 \| 0.00 \| 0.52 \| 0.39 \| 0.44 \| 0.56 \| \| basophil \| 33 \| 0.64 \| 0.61 \| 0.59 \| 0.47 \| 0.00 \| 0.38 \| 0.54 \| 0.62 \| 0.73 \| \| cardiac valve cell \| 22 \| 0.45 \| 0.71 \| 0.51 \| 0.56 \| 0.00 \| 0.58 \| 0.83 \| 0.78 \| 0.72 \| \| oligodendrocyte precursor cell \| 29 \| 0.87 \| 0.95 \| 0.93 \| 0.97 \| 0.00 \| 0.98 \| 0.98 \| 1.00 \| 0.98 \| \| Schwann cell \| 22 \| 0.63 \| 0.67 \| 0.81 \| 0.65 \| 0.00 \| 0.81 \| 0.77 \| 0.67 \| 0.74 \| \| pulmonary interstitial fibroblast \| 24 \| 0.87 \| 0.86 \| 0.70 \| 0.75 \| 0.00 \| 0.92 \| 0.96 \| 0.90 \| 0.92 \| \| smooth muscle cell of trachea \| 25 \| 0.00 \| 0.00 \| 0.11 \| 0.07 \| 0.00 \| 0.23 \| 0.35 \| 0.50 \| 0.36 \| \| granulocyte monocyte progenitor cell \| 28 \| 0.60 \| 0.71 \| 0.56 \| 0.69 \| 0.00 \| 0.47 \| 0.85 \| 0.77 \| 0.85 \| \| interneuron \| 25 \| 0.00 \| 0.53 \| 0.28 \| 0.65 \| 0.00 \| 0.86 \| 0.94 \| 0.78 \| 0.94 \| \| pancreatic PP cell \| 23 \| 0.00 \| 0.23 \| 0.08 \| 0.56 \| 0.00 \| 0.68 \| 0.78 \| 0.76 \| 0.61 \| \| smooth muscle cell of the pulmonary artery \| 26 \| 0.00 \| 0.68 \| 0.56 \| 0.21 \| 0.00 \| 0.33 \| 0.88 \| 0.70 \| 0.73 \| \| cell of skeletal muscle \| 16 \| 0.94 \| 0.94 \| 0.78 \| 0.97 \| 0.00 \| 0.94 \| 0.91 \| 0.94 \| 0.94 \| \| Langerhans cell \| 18 \| 0.88 \| 0.85 \| 0.36 \| 0.38 \| 0.00 \| 0.41 \| 0.90 \| 0.81 \| 0.91 \| \| brush cell of epithelium proper of large intestine \| 10 \| 0.00 \| 0.78 \| 0.00 \| 0.67 \| 0.00 \| 0.62 \| 0.90 \| 0.82 \| 0.80 \| \| stem cell of epidermis \| 11 \| 0.00 \| 0.00 \| 0.50 \| 0.12 \| 0.00 \| 0.36 \| 0.56 \| 0.53 \| 0.62 \| \| regulatory T cell \| 13 \| 0.00 \| 0.00 \| 0.00 \| 0.06 \| 0.00 \| 0.14 \| 0.12 \| 0.21 \| 0.25 \| \| epithelial cell of thymus \| 16 \| 0.00 \| 0.00 \| 0.17 \| 0.36 \| 0.00 \| 0.06 \| 0.52 \| 0.25 \| 0.30 \| \| enteroendocrine cell \| 8 \| 0.67 \| 0.60 \| 0.33 \| 0.59 \| 0.00 \| 0.67 \| 0.89 \| 0.78 \| 0.74 \| \| kidney capillary endothelial cell \| 19 \| 0.17 \| 0.54 \| 0.30 \| 0.18 \| 0.00 \| 0.30 \| 0.26 \| 0.27 \| 0.27 \| \| neuroepithelial stem cell \| 16 \| 0.18 \| 0.21 \| 0.36 \| 0.27 \| 0.00 \| 0.23 \| 0.62 \| 0.29 \| 0.52 \| \| macrophage dendritic cell progenitor \| 9 \| 0.00 \| 0.14 \| 0.52 \| 0.14 \| 0.00 \| 0.52 \| 0.44 \| 0.62 \| 0.70 \| \| DN3 thymocyte \| 16 \| 0.00 \| 0.00 \| 0.00 \| 0.43 \| 0.00 \| 0.79 \| 0.82 \| 0.73 \| 0.52 \| \| regular ventricular cardiac myocyte \| 9 \| 0.00 \| 0.00 \| 0.78 \| 0.48 \| 0.00 \| 0.53 \| 0.84 \| 1.00 \| 0.89 \| \| endothelial cell of lymphatic vessel \| 5 \| 0.00 \| 0.43 \| 0.80 \| 0.10 \| 0.00 \| 0.09 \| 0.34 \| 0.21 \| 0.57 \| \| ciliated columnar cell of tracheobronchial tree \| 16 \| 0.72 \| 0.80 \| 0.85 \| 0.80 \| 0.00 \| 0.59 \| 0.88 \| 0.75 \| 0.88 \| \| plasmacytoid dendritic cell \| 13 \| 0.00 \| 0.56 \| 0.32 \| 0.24 \| 0.00 \| 0.13 \| 0.44 \| 0.29 \| 0.52 \| \| dendritic cell \| 10 \| 0.00 \| 0.25 \| 0.22 \| 0.32 \| 0.00 \| 0.16 \| 0.54 \| 0.30 \| 0.48 \| \| medium spiny neuron \| 14 \| 0.00 \| 0.78 \| 0.58 \| 0.48 \| 0.00 \| 0.93 \| 0.97 \| 0.96 \| 0.88 \| \| pericyte \| 16 \| 0.00 \| 0.94 \| 0.87 \| 0.58 \| 0.00 \| 0.81 \| 1.00 \| 0.94 \| 1.00 \| \| neural stem cell \| 10 \| 0.00 \| 0.82 \| 0.57 \| 0.76 \| 0.00 \| 0.86 \| 0.78 \| 0.73 \| 0.86 \| \| kidney proximal straight tubule epithelial cell \| 8 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.19 \| 0.00 \| \| blood cell \| 9 \| 0.20 \| 0.00 \| 0.36 \| 0.00 \| 0.00 \| 0.00 \| 0.21 \| 0.18 \| 0.32 \| \| immature NK T cell \| 8 \| 0.00 \| 0.22 \| 0.36 \| 0.00 \| 0.00 \| 0.24 \| 0.41 \| 0.35 \| 0.50 \| \| cardiac neuron \| 10 \| 0.00 \| 0.31 \| 0.29 \| 0.12 \| 0.00 \| 0.32 \| 0.74 \| 0.77 \| 0.62 \| \| mast cell \| 12 \| 0.00 \| 0.00 \| 0.15 \| 0.29 \| 0.00 \| 0.11 \| 0.54 \| 0.56 \| 0.56 \| \| mucus secreting cell \| 8 \| 0.00 \| 0.62 \| 0.88 \| 0.50 \| 0.00 \| 0.48 \| 0.78 \| 0.89 \| 0.94 \| \| early pro-B cell \| 6 \| 0.00 \| 0.60 \| 0.00 \| 0.46 \| 0.00 \| 0.19 \| 0.62 \| 0.38 \| 0.83 \| \| pancreatic stellate cell \| 9 \| 0.00 \| 0.75 \| 0.36 \| 0.57 \| 0.00 \| 0.16 \| 0.82 \| 0.84 \| 0.84 \| \| ependymal cell \| 5 \| 0.00 \| 0.75 \| 1.00 \| 0.67 \| 0.00 \| 0.50 \| 0.77 \| 0.77 \| 0.77 \| \| Bergmann glial cell \| 4 \| 0.00 \| 0.40 \| 0.57 \| 0.67 \| 0.00 \| 0.67 \| 1.00 \| 0.89 \| 1.00 \| \| neuroendocrine cell \| 7 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.20 \| 0.14 \| 0.00 \| \| fibrocyte \| 0 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| \| hepatic stellate cell \| 5 \| 0.00 \| 0.00 \| 0.60 \| 0.77 \| 0.00 \| 0.56 \| 0.91 \| 0.83 \| 1.00 \| \| club cell \| 7 \| 0.14 \| 0.25 \| 0.10 \| 0.15 \| 0.00 \| 0.13 \| 0.14 \| 0.13 \| 0.14 \| \| respiratory basal cell \| 2 \| 0.00 \| 0.00 \| 0.00 \| 0.50 \| 0.00 \| 0.11 \| 0.67 \| 0.67 \| 0.67 \| \| kidney interstitial fibroblast \| 1 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.67 \| 1.00 \| 0.00 \| \| lymphoid lineage restricted progenitor cell \| 1 \| 0.00 \| 0.00 \| 1.00 \| 0.67 \| 0.00 \| 0.40 \| 0.03 \| 1.00 \| 1.00 \| \| lung neuroendocrine cell \| 0 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| \| kidney cell \| 0 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| \| duct epithelial cell \| 0 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| The train accuracies are computed on the training data. \| Cell Type \| N cells \| celltypist \| knn bbknn \| knn harmony \| knn on scvi \| onclass \| scanvi \| svm \| xgboost \| Consensus Prediction \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| B cell \| 32653 \| 0.86 \| 0.89 \| 0.88 \| 0.93 \| 0.00 \| 0.95 \| 0.75 \| 0.79 \| 0.94 \| \| mesenchymal stem cell \| 14461 \| 0.86 \| 0.86 \| 0.96 \| 0.87 \| 0.00 \| 0.86 \| 0.92 \| 0.87 \| 0.95 \| \| basal cell of epidermis \| 14140 \| 0.85 \| 0.87 \| 0.91 \| 0.91 \| 0.00 \| 0.85 \| 0.87 \| 0.83 \| 0.92 \| \| granulocyte \| 14006 \| 0.80 \| 0.84 \| 0.90 \| 0.91 \| 0.00 \| 0.87 \| 0.74 \| 0.81 \| 0.91 \| \| endothelial cell \| 13329 \| 0.87 \| 0.86 \| 0.87 \| 0.86 \| 0.00 \| 0.88 \| 0.91 \| 0.85 \| 0.95 \| \| microglial cell \| 11975 \| 0.99 \| 0.99 \| 0.94 \| 0.99 \| 0.00 \| 0.97 \| 0.99 \| 0.99 \| 0.99 \| \| T cell \| 11497 \| 0.75 \| 0.79 \| 0.77 \| 0.85 \| 0.00 \| 0.89 \| 0.65 \| 0.74 \| 0.88 \| \| keratinocyte \| 11101 \| 0.85 \| 0.88 \| 0.91 \| 0.91 \| 0.00 \| 0.88 \| 0.89 \| 0.86 \| 0.92 \| \| macrophage \| 8653 \| 0.48 \| 0.64 \| 0.80 \| 0.76 \| 0.00 \| 0.76 \| 0.57 \| 0.61 \| 0.80 \| \| classical monocyte \| 7359 \| 0.76 \| 0.55 \| 0.80 \| 0.88 \| 0.00 \| 0.90 \| 0.81 \| 0.84 \| 0.92 \| \| granulocytopoietic cell \| 7275 \| 0.66 \| 0.78 \| 0.84 \| 0.85 \| 0.00 \| 0.82 \| 0.69 \| 0.80 \| 0.89 \| \| bladder urothelial cell \| 5958 \| 0.98 \| 0.98 \| 0.99 \| 0.95 \| 0.00 \| 0.99 \| 0.99 \| 0.98 \| 0.99 \| \| mesenchymal stem cell of adipose tissue \| 5973 \| 0.76 \| 0.82 \| 0.89 \| 0.71 \| 0.00 \| 0.91 \| 0.92 \| 0.82 \| 0.94 \| \| fibroblast of cardiac tissue \| 5710 \| 0.94 \| 0.92 \| 0.88 \| 0.73 \| 0.00 \| 0.87 \| 0.96 \| 0.91 \| 0.97 \| \| endothelial cell of coronary artery \| 5363 \| 0.80 \| 0.83 \| 0.79 \| 0.78 \| 0.00 \| 0.91 \| 0.90 \| 0.84 \| 0.96 \| \| skeletal muscle satellite cell \| 4308 \| 0.98 \| 0.98 \| 0.97 \| 0.97 \| 0.00 \| 0.97 \| 0.97 \| 0.97 \| 0.98 \| \| proerythroblast \| 4242 \| 0.51 \| 0.52 \| 0.72 \| 0.75 \| 0.00 \| 0.77 \| 0.57 \| 0.66 \| 0.74 \| \| kidney proximal convoluted tubule epithelial cell \| 4015 \| 0.83 \| 0.83 \| 0.91 \| 0.89 \| 0.00 \| 0.90 \| 0.90 \| 0.89 \| 0.92 \| \| bronchial smooth muscle cell \| 4002 \| 0.95 \| 0.96 \| 0.81 \| 0.93 \| 0.00 \| 0.93 \| 0.97 \| 0.95 \| 0.97 \| \| myeloid cell \| 4007 \| 0.44 \| 0.57 \| 0.73 \| 0.68 \| 0.00 \| 0.84 \| 0.74 \| 0.72 \| 0.85 \| \| hepatocyte \| 3698 \| 0.99 \| 0.98 \| 0.99 \| 0.99 \| 0.00 \| 0.99 \| 0.99 \| 0.99 \| 0.99 \| \| type B pancreatic cell \| 3587 \| 0.97 \| 0.96 \| 0.96 \| 0.98 \| 0.00 \| 0.98 \| 0.99 \| 0.98 \| 0.99 \| \| bladder cell \| 3600 \| 0.97 \| 0.95 \| 0.99 \| 0.75 \| 0.00 \| 0.97 \| 0.98 \| 0.96 \| 0.99 \| \| promonocyte \| 3477 \| 0.65 \| 0.69 \| 0.85 \| 0.83 \| 0.00 \| 0.85 \| 0.78 \| 0.83 \| 0.88 \| \| DN4 thymocyte \| 3331 \| 0.34 \| 0.45 \| 0.60 \| 0.69 \| 0.00 \| 0.88 \| 0.61 \| 0.66 \| 0.80 \| \| basal cell \| 3342 \| 0.98 \| 0.97 \| 0.98 \| 0.94 \| 0.00 \| 0.96 \| 0.98 \| 0.97 \| 0.99 \| \| monocyte \| 3324 \| 0.61 \| 0.53 \| 0.74 \| 0.76 \| 0.00 \| 0.88 \| 0.77 \| 0.81 \| 0.90 \| \| enterocyte of epithelium of large intestine \| 3293 \| 0.91 \| 0.92 \| 0.94 \| 0.93 \| 0.00 \| 0.95 \| 0.95 \| 0.93 \| 0.96 \| \| thymocyte \| 3147 \| 0.80 \| 0.75 \| 0.86 \| 0.92 \| 0.00 \| 0.91 \| 0.91 \| 0.93 \| 0.96 \| \| epithelial cell of proximal tubule \| 3101 \| 0.82 \| 0.78 \| 0.87 \| 0.85 \| 0.00 \| 0.87 \| 0.87 \| 0.86 \| 0.88 \| \| natural killer cell \| 3009 \| 0.82 \| 0.81 \| 0.74 \| 0.83 \| 0.00 \| 0.86 \| 0.80 \| 0.84 \| 0.88 \| \| naive B cell \| 3016 \| 0.44 \| 0.19 \| 0.81 \| 0.66 \| 0.00 \| 0.79 \| 0.52 \| 0.56 \| 0.84 \| \| hematopoietic stem cell \| 3005 \| 0.92 \| 0.93 \| 0.93 \| 0.94 \| 0.00 \| 0.93 \| 0.95 \| 0.94 \| 0.95 \| \| mature NK T cell \| 2840 \| 0.54 \| 0.62 \| 0.61 \| 0.79 \| 0.00 \| 0.81 \| 0.63 \| 0.72 \| 0.78 \| \| fibroblast \| 2834 \| 0.18 \| 0.24 \| 0.75 \| 0.63 \| 0.00 \| 0.74 \| 0.64 \| 0.65 \| 0.77 \| \| stromal cell \| 2792 \| 0.61 \| 0.39 \| 0.97 \| 0.64 \| 0.00 \| 0.93 \| 0.87 \| 0.75 \| 0.93 \| \| keratinocyte stem cell \| 2736 \| 0.92 \| 0.92 \| 0.93 \| 0.92 \| 0.00 \| 0.94 \| 0.95 \| 0.90 \| 0.96 \| \| erythroblast \| 2627 \| 0.59 \| 0.68 \| 0.82 \| 0.85 \| 0.00 \| 0.86 \| 0.69 \| 0.75 \| 0.85 \| \| Kupffer cell \| 2526 \| 0.97 \| 0.94 \| 0.87 \| 0.91 \| 0.00 \| 0.96 \| 0.98 \| 0.97 \| 0.99 \| \| megakaryocyte-erythroid progenitor cell \| 2548 \| 0.59 \| 0.63 \| 0.77 \| 0.76 \| 0.00 \| 0.73 \| 0.64 \| 0.73 \| 0.80 \| \| pancreatic acinar cell \| 2472 \| 0.96 \| 0.95 \| 0.94 \| 0.99 \| 0.00 \| 0.98 \| 0.99 \| 0.99 \| 0.99 \| \| luminal epithelial cell of mammary gland \| 2371 \| 0.92 \| 0.91 \| 0.98 \| 0.93 \| 0.00 \| 0.97 \| 0.97 \| 0.95 \| 0.98 \| \| professional antigen presenting cell \| 2347 \| 0.03 \| 0.01 \| 0.27 \| 0.45 \| 0.00 \| 0.93 \| 0.44 \| 0.40 \| 0.49 \| \| epidermal cell \| 2008 \| 0.87 \| 0.87 \| 0.90 \| 0.90 \| 0.00 \| 0.88 \| 0.88 \| 0.87 \| 0.92 \| \| hematopoietic precursor cell \| 1928 \| 0.62 \| 0.71 \| 0.62 \| 0.79 \| 0.00 \| 0.91 \| 0.74 \| 0.81 \| 0.90 \| \| oligodendrocyte \| 1870 \| 0.96 \| 0.99 \| 0.97 \| 1.00 \| 0.00 \| 0.99 \| 1.00 \| 0.99 \| 0.99 \| \| fibroblast of lung \| 1790 \| 0.96 \| 0.95 \| 0.93 \| 0.93 \| 0.00 \| 0.97 \| 0.98 \| 0.97 \| 0.98 \| \| CD8-positive, alpha-beta T cell \| 1757 \| 0.34 \| 0.48 \| 0.45 \| 0.63 \| 0.00 \| 0.69 \| 0.60 \| 0.63 \| 0.74 \| \| epithelial cell of large intestine \| 1759 \| 0.74 \| 0.79 \| 0.86 \| 0.79 \| 0.00 \| 0.82 \| 0.88 \| 0.86 \| 0.89 \| \| intestinal crypt stem cell \| 1719 \| 0.82 \| 0.86 \| 0.90 \| 0.88 \| 0.00 \| 0.87 \| 0.90 \| 0.87 \| 0.92 \| \| chondrocyte \| 1726 \| 0.61 \| 0.62 \| 0.85 \| 0.83 \| 0.00 \| 0.82 \| 0.88 \| 0.88 \| 0.82 \| \| immature T cell \| 1603 \| 0.66 \| 0.00 \| 0.82 \| 0.91 \| 0.00 \| 0.92 \| 0.89 \| 0.89 \| 0.96 \| \| precursor B cell \| 1579 \| 0.67 \| 0.84 \| 0.91 \| 0.87 \| 0.00 \| 0.87 \| 0.85 \| 0.88 \| 0.92 \| \| intermediate monocyte \| 1594 \| 0.35 \| 0.07 \| 0.52 \| 0.67 \| 0.00 \| 0.68 \| 0.57 \| 0.62 \| 0.74 \| \| large intestine goblet cell \| 1582 \| 0.81 \| 0.84 \| 0.92 \| 0.85 \| 0.00 \| 0.91 \| 0.95 \| 0.92 \| 0.95 \| \| CD4-positive, alpha-beta T cell \| 1557 \| 0.26 \| 0.37 \| 0.47 \| 0.61 \| 0.00 \| 0.59 \| 0.48 \| 0.57 \| 0.73 \| \| smooth muscle cell \| 1536 \| 0.61 \| 0.76 \| 0.83 \| 0.86 \| 0.00 \| 0.89 \| 0.90 \| 0.83 \| 0.84 \| \| leukocyte \| 1502 \| 0.52 \| 0.48 \| 0.69 \| 0.62 \| 0.00 \| 0.83 \| 0.68 \| 0.73 \| 0.91 \| \| plasma cell \| 1503 \| 0.52 \| 0.62 \| 0.80 \| 0.81 \| 0.00 \| 0.76 \| 0.65 \| 0.67 \| 0.79 \| \| lymphocyte \| 1428 \| 0.23 \| 0.23 \| 0.69 \| 0.61 \| 0.00 \| 0.86 \| 0.32 \| 0.47 \| 0.74 \| \| naive T cell \| 1422 \| 0.82 \| 0.86 \| 0.92 \| 0.92 \| 0.00 \| 0.90 \| 0.89 \| 0.91 \| 0.92 \| \| kidney loop of Henle thick ascending limb epithelial cell \| 1413 \| 0.92 \| 0.92 \| 0.94 \| 0.88 \| 0.00 \| 0.90 \| 0.95 \| 0.96 \| 0.96 \| \| immature B cell \| 1268 \| 0.70 \| 0.70 \| 0.77 \| 0.78 \| 0.00 \| 0.70 \| 0.62 \| 0.75 \| 0.89 \| \| alveolar macrophage \| 1232 \| 0.95 \| 0.95 \| 0.95 \| 0.93 \| 0.00 \| 0.94 \| 0.97 \| 0.96 \| 0.98 \| \| endothelial cell of hepatic sinusoid \| 1168 \| 0.96 \| 0.95 \| 0.88 \| 0.88 \| 0.00 \| 0.94 \| 0.97 \| 0.97 \| 0.98 \| \| lung macrophage \| 1060 \| 0.83 \| 0.04 \| 0.68 \| 0.92 \| 0.00 \| 0.93 \| 0.90 \| 0.86 \| 0.96 \| \| non-classical monocyte \| 1045 \| 0.56 \| 0.68 \| 0.85 \| 0.82 \| 0.00 \| 0.91 \| 0.84 \| 0.78 \| 0.86 \| \| endocardial cell \| 940 \| 0.92 \| 0.91 \| 0.93 \| 0.88 \| 0.00 \| 0.79 \| 0.96 \| 0.87 \| 0.96 \| \| double negative thymocyte \| 866 \| 0.57 \| 0.00 \| 0.63 \| 0.76 \| 0.00 \| 0.84 \| 0.79 \| 0.82 \| 0.91 \| \| late pro-B cell \| 851 \| 0.78 \| 0.78 \| 0.86 \| 0.85 \| 0.00 \| 0.88 \| 0.78 \| 0.87 \| 0.95 \| \| kidney collecting duct principal cell \| 837 \| 0.69 \| 0.88 \| 0.93 \| 0.89 \| 0.00 \| 0.92 \| 0.97 \| 0.96 \| 0.96 \| \| basal epithelial cell of tracheobronchial tree \| 838 \| 0.86 \| 0.73 \| 0.94 \| 0.81 \| 0.00 \| 0.75 \| 0.94 \| 0.87 \| 0.96 \| \| mesenchymal cell \| 788 \| 0.19 \| 0.00 \| 0.43 \| 0.29 \| 0.00 \| 0.40 \| 0.53 \| 0.57 \| 0.53 \| \| pancreatic A cell \| 750 \| 0.63 \| 0.72 \| 0.85 \| 0.89 \| 0.00 \| 0.87 \| 0.95 \| 0.94 \| 0.93 \| \| adventitial cell \| 752 \| 0.65 \| 0.54 \| 0.90 \| 0.42 \| 0.00 \| 0.82 \| 0.80 \| 0.69 \| 0.91 \| \| epithelial cell \| 712 \| 0.40 \| 0.03 \| 0.84 \| 0.67 \| 0.00 \| 0.76 \| 0.67 \| 0.69 \| 0.82 \| \| kidney distal convoluted tubule epithelial cell \| 664 \| 0.80 \| 0.87 \| 0.92 \| 0.80 \| 0.00 \| 0.92 \| 0.97 \| 0.97 \| 0.95 \| \| neutrophil \| 632 \| 0.50 \| 0.47 \| 0.76 \| 0.79 \| 0.00 \| 0.58 \| 0.68 \| 0.63 \| 0.80 \| \| pancreatic ductal cell \| 594 \| 0.98 \| 0.78 \| 0.98 \| 0.95 \| 0.00 \| 0.96 \| 0.97 \| 0.97 \| 0.98 \| \| myeloid dendritic cell \| 564 \| 0.34 \| 0.52 \| 0.82 \| 0.60 \| 0.00 \| 0.78 \| 0.78 \| 0.65 \| 0.75 \| \| astrocyte \| 522 \| 0.89 \| 0.93 \| 0.97 \| 0.95 \| 0.00 \| 0.96 \| 0.99 \| 0.99 \| 0.98 \| \| secretory cell \| 511 \| 0.26 \| 0.33 \| 0.81 \| 0.69 \| 0.00 \| 0.85 \| 0.93 \| 0.89 \| 0.93 \| \| myeloid leukocyte \| 456 \| 0.57 \| 0.31 \| 0.82 \| 0.57 \| 0.00 \| 0.84 \| 0.55 \| 0.68 \| 0.92 \| \| plasmatocyte \| 448 \| 0.57 \| 0.81 \| 0.90 \| 0.84 \| 0.00 \| 0.90 \| 0.94 \| 0.87 \| 0.96 \| \| brain pericyte \| 428 \| 0.00 \| 0.73 \| 0.67 \| 0.87 \| 0.00 \| 0.94 \| 0.96 \| 0.92 \| 0.99 \| \| neuron \| 436 \| 0.62 \| 0.59 \| 0.81 \| 0.80 \| 0.00 \| 0.90 \| 0.96 \| 0.91 \| 0.95 \| \| vein endothelial cell \| 423 \| 0.62 \| 0.76 \| 0.82 \| 0.65 \| 0.00 \| 0.74 \| 0.91 \| 0.83 \| 0.92 \| \| kidney collecting duct epithelial cell \| 414 \| 0.00 \| 0.51 \| 0.89 \| 0.81 \| 0.00 \| 0.92 \| 0.85 \| 0.90 \| 0.95 \| \| pancreatic D cell \| 417 \| 0.00 \| 0.64 \| 0.84 \| 0.89 \| 0.00 \| 0.94 \| 0.97 \| 0.96 \| 0.98 \| \| aortic endothelial cell \| 420 \| 0.16 \| 0.00 \| 0.30 \| 0.39 \| 0.00 \| 0.71 \| 0.39 \| 0.32 \| 0.68 \| \| brush cell \| 377 \| 0.82 \| 0.60 \| 0.94 \| 0.91 \| 0.00 \| 0.92 \| 0.86 \| 0.85 \| 0.91 \| \| erythroid progenitor cell, mammalian \| 361 \| 0.59 \| 0.00 \| 0.67 \| 0.80 \| 0.00 \| 0.81 \| 0.79 \| 0.66 \| 0.94 \| \| regular atrial cardiac myocyte \| 362 \| 0.00 \| 0.40 \| 0.69 \| 0.70 \| 0.00 \| 0.58 \| 0.70 \| 0.75 \| 0.86 \| \| kidney cortex artery cell \| 341 \| 0.00 \| 0.83 \| 0.76 \| 0.52 \| 0.00 \| 0.62 \| 0.83 \| 0.67 \| 0.84 \| \| erythrocyte \| 312 \| 0.61 \| 0.47 \| 0.71 \| 0.86 \| 0.00 \| 0.84 \| 0.83 \| 0.87 \| 0.94 \| \| mesangial cell \| 322 \| 0.00 \| 0.69 \| 0.69 \| 0.69 \| 0.00 \| 0.85 \| 0.88 \| 0.80 \| 0.95 \| \| kidney loop of Henle ascending limb epithelial cell \| 312 \| 0.47 \| 0.59 \| 0.69 \| 0.56 \| 0.00 \| 0.74 \| 0.86 \| 0.85 \| 0.78 \| \| cardiac muscle cell \| 302 \| 0.60 \| 0.68 \| 0.87 \| 0.96 \| 0.00 \| 1.00 \| 0.99 \| 0.97 \| 0.98 \| \| mature alpha-beta T cell \| 293 \| 0.00 \| 0.08 \| 0.40 \| 0.41 \| 0.00 \| 0.68 \| 0.62 \| 0.62 \| 0.88 \| \| basophil \| 292 \| 0.63 \| 0.63 \| 0.88 \| 0.81 \| 0.00 \| 0.68 \| 0.69 \| 0.75 \| 0.84 \| \| cardiac valve cell \| 293 \| 0.66 \| 0.83 \| 0.75 \| 0.82 \| 0.00 \| 0.83 \| 0.94 \| 0.91 \| 0.92 \| \| oligodendrocyte precursor cell \| 283 \| 0.91 \| 0.96 \| 0.98 \| 0.97 \| 0.00 \| 0.97 \| 1.00 \| 0.99 \| 1.00 \| \| Schwann cell \| 252 \| 0.72 \| 0.71 \| 0.82 \| 0.86 \| 0.00 \| 0.92 \| 0.86 \| 0.82 \| 0.82 \| \| pulmonary interstitial fibroblast \| 248 \| 0.87 \| 0.82 \| 0.91 \| 0.87 \| 0.00 \| 0.91 \| 0.96 \| 0.96 \| 0.96 \| \| smooth muscle cell of trachea \| 227 \| 0.00 \| 0.04 \| 0.46 \| 0.57 \| 0.00 \| 0.64 \| 0.65 \| 0.74 \| 0.85 \| \| granulocyte monocyte progenitor cell \| 222 \| 0.50 \| 0.57 \| 0.86 \| 0.80 \| 0.00 \| 0.63 \| 0.81 \| 0.74 \| 0.85 \| \| interneuron \| 215 \| 0.00 \| 0.55 \| 0.82 \| 0.76 \| 0.00 \| 0.90 \| 0.97 \| 0.92 \| 0.95 \| \| pancreatic PP cell \| 205 \| 0.00 \| 0.06 \| 0.69 \| 0.78 \| 0.00 \| 0.80 \| 0.92 \| 0.93 \| 0.92 \| \| smooth muscle cell of the pulmonary artery \| 183 \| 0.00 \| 0.69 \| 0.81 \| 0.57 \| 0.00 \| 0.35 \| 0.89 \| 0.75 \| 0.88 \| \| cell of skeletal muscle \| 176 \| 0.95 \| 0.92 \| 0.77 \| 0.93 \| 0.00 \| 0.91 \| 0.96 \| 0.95 \| 0.96 \| \| Langerhans cell \| 157 \| 0.83 \| 0.80 \| 0.90 \| 0.79 \| 0.00 \| 0.65 \| 0.87 \| 0.78 \| 0.96 \| \| brush cell of epithelium proper of large intestine \| 165 \| 0.00 \| 0.70 \| 0.85 \| 0.85 \| 0.00 \| 0.81 \| 0.99 \| 0.95 \| 0.95 \| \| stem cell of epidermis \| 160 \| 0.00 \| 0.00 \| 0.72 \| 0.64 \| 0.00 \| 0.40 \| 0.67 \| 0.64 \| 0.85 \| \| regulatory T cell \| 157 \| 0.00 \| 0.03 \| 0.23 \| 0.30 \| 0.00 \| 0.41 \| 0.46 \| 0.38 \| 0.76 \| \| epithelial cell of thymus \| 149 \| 0.00 \| 0.01 \| 0.75 \| 0.53 \| 0.00 \| 0.39 \| 0.79 \| 0.61 \| 0.92 \| \| enteroendocrine cell \| 153 \| 0.84 \| 0.74 \| 0.86 \| 0.72 \| 0.00 \| 0.95 \| 0.97 \| 0.96 \| 0.96 \| \| kidney capillary endothelial cell \| 142 \| 0.17 \| 0.49 \| 0.33 \| 0.37 \| 0.00 \| 0.28 \| 0.27 \| 0.34 \| 0.29 \| \| neuroepithelial stem cell \| 135 \| 0.21 \| 0.11 \| 0.88 \| 0.67 \| 0.00 \| 0.60 \| 0.88 \| 0.67 \| 0.87 \| \| macrophage dendritic cell progenitor \| 139 \| 0.00 \| 0.29 \| 0.81 \| 0.79 \| 0.00 \| 0.79 \| 0.73 \| 0.75 \| 0.90 \| \| DN3 thymocyte \| 130 \| 0.00 \| 0.00 \| 0.48 \| 0.77 \| 0.00 \| 0.76 \| 0.77 \| 0.80 \| 0.90 \| \| regular ventricular cardiac myocyte \| 137 \| 0.00 \| 0.00 \| 0.90 \| 0.86 \| 0.00 \| 0.65 \| 0.97 \| 0.98 \| 0.99 \| \| endothelial cell of lymphatic vessel \| 135 \| 0.00 \| 0.45 \| 0.95 \| 0.57 \| 0.00 \| 0.44 \| 0.74 \| 0.51 \| 0.91 \| \| ciliated columnar cell of tracheobronchial tree \| 111 \| 0.82 \| 0.76 \| 0.84 \| 0.90 \| 0.00 \| 0.73 \| 0.98 \| 0.94 \| 0.98 \| \| plasmacytoid dendritic cell \| 109 \| 0.00 \| 0.29 \| 0.87 \| 0.67 \| 0.00 \| 0.46 \| 0.66 \| 0.55 \| 0.78 \| \| dendritic cell \| 97 \| 0.00 \| 0.35 \| 0.77 \| 0.54 \| 0.00 \| 0.53 \| 0.80 \| 0.62 \| 0.83 \| \| medium spiny neuron \| 89 \| 0.00 \| 0.00 \| 0.86 \| 0.79 \| 0.00 \| 0.89 \| 1.00 \| 0.99 \| 0.99 \| \| pericyte \| 83 \| 0.00 \| 0.85 \| 0.83 \| 0.77 \| 0.00 \| 0.91 \| 0.86 \| 0.90 \| 0.93 \| \| neural stem cell \| 87 \| 0.00 \| 0.65 \| 0.85 \| 0.87 \| 0.00 \| 0.93 \| 0.96 \| 0.97 \| 0.98 \| \| kidney proximal straight tubule epithelial cell \| 87 \| 0.00 \| 0.00 \| 0.14 \| 0.00 \| 0.00 \| 0.52 \| 0.73 \| 0.43 \| 0.36 \| \| blood cell \| 79 \| 0.16 \| 0.00 \| 0.43 \| 0.28 \| 0.00 \| 0.20 \| 0.38 \| 0.35 \| 0.38 \| \| immature NK T cell \| 76 \| 0.00 \| 0.09 \| 0.32 \| 0.38 \| 0.00 \| 0.50 \| 0.53 \| 0.50 \| 0.72 \| \| cardiac neuron \| 71 \| 0.00 \| 0.08 \| 0.72 \| 0.63 \| 0.00 \| 0.72 \| 0.78 \| 0.76 \| 0.87 \| \| mast cell \| 64 \| 0.00 \| 0.00 \| 0.59 \| 0.82 \| 0.00 \| 0.35 \| 0.66 \| 0.62 \| 0.79 \| \| mucus secreting cell \| 65 \| 0.00 \| 0.34 \| 0.78 \| 0.68 \| 0.00 \| 0.85 \| 0.90 \| 0.97 \| 0.98 \| \| early pro-B cell \| 66 \| 0.00 \| 0.48 \| 0.76 \| 0.72 \| 0.00 \| 0.39 \| 0.79 \| 0.68 \| 0.87 \| \| pancreatic stellate cell \| 60 \| 0.00 \| 0.61 \| 0.93 \| 0.40 \| 0.00 \| 0.36 \| 0.79 \| 0.81 \| 0.96 \| \| ependymal cell \| 50 \| 0.00 \| 0.59 \| 0.95 \| 0.89 \| 0.00 \| 0.65 \| 1.00 \| 1.00 \| 1.00 \| \| Bergmann glial cell \| 50 \| 0.00 \| 0.43 \| 0.86 \| 0.81 \| 0.00 \| 0.57 \| 1.00 \| 1.00 \| 0.95 \| \| neuroendocrine cell \| 41 \| 0.00 \| 0.00 \| 0.20 \| 0.49 \| 0.00 \| 0.70 \| 0.98 \| 0.77 \| 0.59 \| \| fibrocyte \| 39 \| 0.00 \| 0.00 \| 0.30 \| 0.31 \| 0.00 \| 0.41 \| 0.80 \| 0.76 \| 0.77 \| \| hepatic stellate cell \| 34 \| 0.00 \| 0.21 \| 0.52 \| 0.77 \| 0.00 \| 0.54 \| 0.91 \| 0.93 \| 0.96 \| \| club cell \| 30 \| 0.11 \| 0.45 \| 0.19 \| 0.11 \| 0.00 \| 0.17 \| 0.19 \| 0.18 \| 0.16 \| \| respiratory basal cell \| 32 \| 0.00 \| 0.00 \| 0.95 \| 0.75 \| 0.00 \| 0.26 \| 0.96 \| 0.89 \| 0.95 \| \| kidney interstitial fibroblast \| 15 \| 0.00 \| 0.00 \| 0.97 \| 0.77 \| 0.00 \| 0.40 \| 0.75 \| 0.83 \| 1.00 \| \| lymphoid lineage restricted progenitor cell \| 12 \| 0.00 \| 0.00 \| 0.91 \| 0.53 \| 0.00 \| 0.56 \| 0.04 \| 0.86 \| 0.91 \| \| lung neuroendocrine cell \| 8 \| 0.00 \| 0.00 \| 0.82 \| 0.22 \| 0.00 \| 0.64 \| 0.21 \| 1.00 \| 0.86 \| \| kidney cell \| 3 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.16 \| 0.05 \| 0.50 \| 0.50 \| \| duct epithelial cell \| 2 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.00 \| 0.10 \| 0.31 \| 0.67 \| 0.00 \| </details> # References A single-cell transcriptomic atlas characterizes ageing tissues in the mouse, The Tabula Muris Consortium, Nicole Almanzar, Jane Antony, Ankit S. Baghel, Isaac Bakerman, Ishita Bansal, Ben A. Barres, Philip A. Beachy, Daniela Berdnik, Biter Bilen, Douglas Brownfield, Corey Cain, Charles K. F. Chan, Michelle B. Chen, Michael F. Clarke, Stephanie D. Conley, Spyros Darmanis, Aaron Demers, Kubilay Demir, Antoine de Morree, Tessa Divita, Haley du Bois, Hamid Ebadi, F. Hernán Espinoza, Matt Fish, Qiang Gan, Benson M. George, Astrid Gillich, Rafael Gòmez-Sjöberg, Foad Green, Geraldine Genetiano, Xueying Gu, Gunsagar S. Gulati, Oliver Hahn, Michael Seamus Haney, Yan Hang, Lincoln Harris, Mu He, Shayan Hosseinzadeh, Albin Huang, Kerwyn Casey Huang, Tal Iram, Taichi Isobe, Feather Ives, Robert C. Jones, Kevin S. Kao, Jim Karkanias, Guruswamy Karnam, Andreas Keller, Aaron M. Kershner, Nathalie Khoury, Seung K. Kim, Bernhard M. Kiss, William Kong, Mark A. Krasnow, Maya E. Kumar, Christin S. Kuo, Jonathan Lam, Davis P. Lee, Song E. Lee, Benoit Lehallier, Olivia Leventhal, Guang Li, Qingyun Li, Ling Liu, Annie Lo, Wan-Jin Lu, Maria F. Lugo-Fagundo, Anoop Manjunath, Andrew P. May, Ashley Maynard, Aaron McGeever, Marina McKay, M. Windy McNerney, Bryan Merrill, Ross J. Metzger, Marco Mignardi, Dullei Min, Ahmad N. Nabhan, Norma F. Neff, Katharine M. Ng, Patricia K. Nguyen, Joseph Noh, Roel Nusse, Róbert Pálovics, Rasika Patkar, Weng Chuan Peng, Lolita Penland, Angela Oliveira Pisco, Katherine Pollard, Robert Puccinelli, Zhen Qi, Stephen R. Quake, Thomas A. Rando, Eric J. Rulifson, Nicholas Schaum, Joe M. Segal, Shaheen S. Sikandar, Rahul Sinha, Rene V. Sit, Justin Sonnenburg, Daniel Staehli, Krzysztof Szade, Michelle Tan, Weilun Tan, Cristina Tato, Krissie Tellez, Laughing Bear Torrez Dulgeroff, Kyle J. Travaglini, Carolina Tropini, Margaret Tsui, Lucas Waldburger, Bruce M. Wang, Linda J. van Weele, Kenneth Weinberg, Irving L. Weissman, Michael N. Wosczyna, Sean M. Wu, Tony Wyss-Coray, Jinyi Xiang, Soso Xue, Kevin A. Yamauchi, Andrew C. Yang, Lakshmi P. Yerra, Justin Youngyunpipatkul, Brian Yu, Fabio Zanini, Macy E. Zardeneta, Alexander Zee, Chunyu Zhao, Fan Zhang, Hui Zhang, Martin Jinye Zhang, Lu Zhou, James Zou; Nature, doi: https://doi.org/10.1038/s41586-020-2496-1
Holarissun/SFT_gemma2b_hh-rlhf-helpful_lr5e-06_epoch2-subset-1	Holarissun	"2024-06-17T16:51:51Z"	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	"2024-06-17T16:51:48Z"	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b model-index: - name: SFT_gemma2b_hh-rlhf-helpful_lr5e-06_epoch2-subset-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SFT_gemma2b_hh-rlhf-helpful_lr5e-06_epoch2-subset-1 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
zenless-lab/sdxl-anything-xl	zenless-lab	"2024-10-31T13:35:02Z"	30	0	diffusers	[ "diffusers", "safetensors", "anime", "text-to-image", "arxiv:1910.09700", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2024-10-31T13:02:51Z"	--- library_name: diffusers license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ base_model: - stabilityai/stable-diffusion-xl-base-1.0 pipeline_tag: text-to-image tags: - anime --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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prithivMLmods/Blaze.1-32B-Instruct	prithivMLmods	"2025-01-15T11:23:54Z"	114	7	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "conversational", "en", "base_model:Qwen/QwQ-32B-Preview", "base_model:finetune:Qwen/QwQ-32B-Preview", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-12T18:24:54Z"	--- license: apache-2.0 language: - en base_model: - Qwen/QwQ-32B-Preview pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference --- ![7.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xOHhPF_5HZcxDzxxeLv6x.png) # Blaze.1-32B-Instruct Blaze.1-32B-Instruct is based on the QwQ-32B-Preview model, fine-tuned using synthetic data for mathematical reasoning and conditional reasoning to handle complex reasoning problems. The model may unexpectedly mix languages or switch between them, affecting response clarity. Additionally, it may enter recursive reasoning loops, resulting in lengthy responses without a conclusive answer, as it focuses on maintaining a continuous chain of thought reasoning. # Quickstart Chat Template Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Blaze.1-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "How many r in strawberry." messages = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` # Intended Use Blaze.1-32B-Instruct is designed to assist with complex reasoning tasks, including mathematical problem-solving, logical reasoning, and step-by-step explanations. It is particularly useful for applications requiring conditional reasoning, structured content generation, and language understanding across multiple domains. The model is also fine-tuned for conversational AI, making it well-suited for virtual assistants, educational tools, and research purposes. Additionally, it supports tasks involving multilingual understanding, making it valuable in environments where language switching or code-mixed text processing is required. # Limitations 1. Language Mixing and Code-Switching Issues: The model may unexpectedly switch between languages or mix them within a single response, potentially reducing the clarity of outputs. 2. Recursive Reasoning Loops: During complex reasoning, the model may enter circular reasoning patterns, resulting in overly lengthy responses without arriving at a definitive conclusion. 3. Overfitting to Training Data: Since Blaze.1-32B-Instruct is fine-tuned on specific synthetic datasets, its performance might be biased toward certain types of problems and may generalize poorly on entirely new tasks. 4. Context Sensitivity: While the model is trained for step-by-step reasoning, it may occasionally lose track of the context in longer conversations, leading to irrelevant or incomplete answers. 5. Resource Intensity**: As a large model (32B parameters), it requires significant computational resources for both inference and deployment, which may limit its usability in low-resource environments.
QuantFactory/Tulu-3.1-8B-SuperNova-GGUF	QuantFactory	"2024-12-01T13:26:34Z"	294	3	transformers	[ "transformers", "gguf", "mergekit", "merge", "arxiv:2203.05482", "base_model:allenai/Llama-3.1-Tulu-3-8B", "base_model:merge:allenai/Llama-3.1-Tulu-3-8B", "base_model:arcee-ai/Llama-3.1-SuperNova-Lite", "base_model:merge:arcee-ai/Llama-3.1-SuperNova-Lite", "base_model:meditsolutions/Llama-3.1-MedIT-SUN-8B", "base_model:merge:meditsolutions/Llama-3.1-MedIT-SUN-8B", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-12-01T12:47:10Z"	--- library_name: transformers tags: - mergekit - merge base_model: - meditsolutions/Llama-3.1-MedIT-SUN-8B - allenai/Llama-3.1-Tulu-3-8B - arcee-ai/Llama-3.1-SuperNova-Lite model-index: - name: Tulu-3.1-8B-SuperNova results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 81.94 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 32.5 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 24.32 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 6.94 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 8.69 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 31.27 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova name: Open LLM Leaderboard --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Tulu-3.1-8B-SuperNova-GGUF This is quantized version of [bunnycore/Tulu-3.1-8B-SuperNova](https://huggingface.co/bunnycore/Tulu-3.1-8B-SuperNova) created using llama.cpp # Original Model Card # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [meditsolutions/Llama-3.1-MedIT-SUN-8B](https://huggingface.co/meditsolutions/Llama-3.1-MedIT-SUN-8B) * [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) * [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: arcee-ai/Llama-3.1-SuperNova-Lite parameters: weight: 1.0 - model: allenai/Llama-3.1-Tulu-3-8B parameters: weight: 1.0 - model: meditsolutions/Llama-3.1-MedIT-SUN-8B parameters: weight: 1.0 merge_method: linear normalize: false int8_mask: true dtype: bfloat16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_bunnycore__Tulu-3.1-8B-SuperNova) \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|30.94\| \|IFEval (0-Shot) \|81.94\| \|BBH (3-Shot) \|32.50\| \|MATH Lvl 5 (4-Shot)\|24.32\| \|GPQA (0-shot) \| 6.94\| \|MuSR (0-shot) \| 8.69\| \|MMLU-PRO (5-shot) \|31.27\|
ty00/gpt2-wikitext2	ty00	"2023-05-31T08:17:47Z"	173	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-05-31T07:20:01Z"	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.5582 \| 1.0 \| 2249 \| 6.4717 \| \| 6.1906 \| 2.0 \| 4498 \| 6.2014 \| \| 6.014 \| 3.0 \| 6747 \| 6.1114 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
microsoft/layoutlmv3-large	microsoft	"2022-09-16T03:26:15Z"	157,275	102	transformers	[ "transformers", "pytorch", "tf", "layoutlmv3", "en", "arxiv:2204.08387", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	null	"2022-04-18T06:56:58Z"	--- language: en license: cc-by-nc-sa-4.0 --- # LayoutLMv3 [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) \| [GitHub](https://aka.ms/layoutlmv3) ## Model description LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis. [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022. ## Citation If you find LayoutLM useful in your research, please cite the following paper: ``` @inproceedings{huang2022layoutlmv3, author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei}, title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking}, booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, year={2022} } ``` ## License The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Portions of the source code are based on the [transformers](https://github.com/huggingface/transformers) project. [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
wongyu/koalpaca-polyglot-12.8b-bill	wongyu	"2023-09-26T07:46:49Z"	0	0	peft	[ "peft", "region:us" ]	null	"2023-09-26T07:46:45Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
DaneshwariS/llama-3.2-gptq-finetuned	DaneshwariS	"2025-03-01T11:03:05Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:saul95/Llama-3.2-1B-GPTQ", "base_model:finetune:saul95/Llama-3.2-1B-GPTQ", "endpoints_compatible", "region:us" ]	null	"2025-03-01T11:03:01Z"	--- base_model: saul95/Llama-3.2-1B-GPTQ library_name: transformers model_name: llama-3.2-gptq-finetuned tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama-3.2-gptq-finetuned This model is a fine-tuned version of [saul95/Llama-3.2-1B-GPTQ](https://huggingface.co/saul95/Llama-3.2-1B-GPTQ). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="DaneshwariS/llama-3.2-gptq-finetuned", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.3 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
djuna/Gemma-2-gemmama-9b-Q5_K_M-GGUF	djuna	"2024-09-08T00:09:57Z"	6	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:djuna/Gemma-2-gemmama-9b", "base_model:quantized:djuna/Gemma-2-gemmama-9b", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-08-31T06:39:26Z"	--- base_model: djuna/Gemma-2-gemmama-9b library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # djuna/Gemma-2-gemmama-9b-Q5_K_M-GGUF This model was converted to GGUF format from [`djuna/Gemma-2-gemmama-9b`](https://huggingface.co/djuna/Gemma-2-gemmama-9b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/djuna/Gemma-2-gemmama-9b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo djuna/Gemma-2-gemmama-9b-Q5_K_M-GGUF --hf-file gemma-2-gemmama-9b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo djuna/Gemma-2-gemmama-9b-Q5_K_M-GGUF --hf-file gemma-2-gemmama-9b-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo djuna/Gemma-2-gemmama-9b-Q5_K_M-GGUF --hf-file gemma-2-gemmama-9b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo djuna/Gemma-2-gemmama-9b-Q5_K_M-GGUF --hf-file gemma-2-gemmama-9b-q5_k_m.gguf -c 2048 ```
Trelis/Llama-3.2-1B-Instruct-touch-rugby-synth-1epochs	Trelis	"2024-10-08T12:55:40Z"	123	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:finetune:unsloth/Llama-3.2-1B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-10-08T12:54:25Z"	--- base_model: unsloth/Llama-3.2-1B-Instruct language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: Trelis - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
theojolliffe/bart-large-cnn-finetuned-roundup-2-1	theojolliffe	"2022-05-04T16:57:42Z"	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2022-05-04T16:34:56Z"	--- license: mit tags: - generated_from_trainer model-index: - name: bart-large-cnn-finetuned-roundup-2-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-cnn-finetuned-roundup-2-1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 167 \| 1.2456 \| 51.7546 \| 32.4725 \| 33.0461 \| 49.0513 \| 142.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
lesso10/bfe8d9f9-bd63-475d-a645-fa1a86e253bb	lesso10	"2025-03-27T22:59:14Z"	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "custom_code", "base_model:NovaSearch/stella_en_1.5B_v5", "base_model:adapter:NovaSearch/stella_en_1.5B_v5", "license:mit", "region:us" ]	null	"2025-03-27T21:36:14Z"	--- library_name: peft license: mit base_model: dunzhang/stella_en_1.5B_v5 tags: - axolotl - generated_from_trainer model-index: - name: bfe8d9f9-bd63-475d-a645-fa1a86e253bb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: dunzhang/stella_en_1.5B_v5 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - be73e9dba600ca71_train_data.json ds_type: json format: custom path: /workspace/input_data/be73e9dba600ca71_train_data.json type: field_instruction: problem field_output: qwq format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso10/bfe8d9f9-bd63-475d-a645-fa1a86e253bb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.00021 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/be73e9dba600ca71_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 100 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 5ca65d3e-d245-41db-87e9-e40832b9c43f wandb_project: 10a wandb_run: your_name wandb_runid: 5ca65d3e-d245-41db-87e9-e40832b9c43f warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # bfe8d9f9-bd63-475d-a645-fa1a86e253bb This model is a fine-tuned version of [dunzhang/stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00021 - train_batch_size: 4 - eval_batch_size: 4 - seed: 100 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0003 \| 1 \| nan \| \| 0.0 \| 0.1277 \| 500 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ckauth/q-Taxi-v3	ckauth	"2022-12-29T14:18:50Z"	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2022-12-29T14:03:32Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ckauth/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
kaiku03/vit-base-1stGen-Pokemon-Images	kaiku03	"2024-08-28T06:09:33Z"	9	0	null	[ "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "region:us" ]	image-classification	"2024-08-27T04:33:13Z"	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-1stGen-Pokemon-Images results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-1stGen-Pokemon-Images This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Dusduo/1stGen-Pokemon-Images dataset. It achieves the following results on the evaluation set: - Loss: 1.0561 - Accuracy: 0.8175 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.4052 \| 1.0 \| 422 \| 2.2029 \| 0.6386 \| \| 1.4347 \| 2.0 \| 844 \| 1.4776 \| 0.7536 \| \| 0.9834 \| 3.0 \| 1266 \| 1.2078 \| 0.7950 \| \| 0.8073 \| 4.0 \| 1688 \| 1.0888 \| 0.8187 \| \| 0.6758 \| 5.0 \| 2110 \| 1.0561 \| 0.8175 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1
w11wo/indonesian-roberta-base-sentiment-classifier	w11wo	"2023-05-13T04:10:11Z"	10,723	34	transformers	[ "transformers", "pytorch", "tf", "safetensors", "roberta", "text-classification", "indonesian-roberta-base-sentiment-classifier", "id", "dataset:indonlu", "arxiv:1907.11692", "doi:10.57967/hf/2997", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2022-03-02T23:29:05Z"	--- language: id tags: - indonesian-roberta-base-sentiment-classifier license: mit datasets: - indonlu widget: - text: "Jangan sampai saya telpon bos saya ya!" --- ## Indonesian RoBERTa Base Sentiment Classifier Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indonlu`](https://hf.co/datasets/indonlu)'s `SmSA` dataset consisting of Indonesian comments and reviews. After training, the model achieved an evaluation accuracy of 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%. Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless. ## Model \| Model \| #params \| Arch. \| Training/Validation data (text) \| \| ---------------------------------------------- \| ------- \| ------------ \| ------------------------------- \| \| `indonesian-roberta-base-sentiment-classifier` \| 124M \| RoBERTa Base \| `SmSA` \| ## Evaluation Results The model was trained for 5 epochs and the best model was loaded at the end. \| Epoch \| Training Loss \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \| ----- \| ------------- \| --------------- \| -------- \| -------- \| --------- \| -------- \| \| 1 \| 0.342600 \| 0.213551 \| 0.928571 \| 0.898539 \| 0.909803 \| 0.890694 \| \| 2 \| 0.190700 \| 0.213466 \| 0.934127 \| 0.901135 \| 0.925297 \| 0.882757 \| \| 3 \| 0.125500 \| 0.219539 \| 0.942857 \| 0.920901 \| 0.927511 \| 0.915193 \| \| 4 \| 0.083600 \| 0.235232 \| 0.943651 \| 0.924227 \| 0.926494 \| 0.922048 \| \| 5 \| 0.059200 \| 0.262473 \| 0.942063 \| 0.920583 \| 0.924084 \| 0.917351 \| ## How to Use ### As Text Classifier ```python from transformers import pipeline pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier" nlp = pipeline( "sentiment-analysis", model=pretrained_name, tokenizer=pretrained_name ) nlp("Jangan sampai saya telpon bos saya ya!") ``` ## Disclaimer Do consider the biases which come from both the pre-trained RoBERTa model and the `SmSA` dataset that may be carried over into the results of this model. ## Author Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. ## Citation If used, please cite the following: ```bibtex @misc {wilson_wongso_2023, author = { {Wilson Wongso} }, title = { indonesian-roberta-base-sentiment-classifier (Revision e402e46) }, year = 2023, url = { https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier }, doi = { 10.57967/hf/0644 }, publisher = { Hugging Face } } ```
AvinashAmballa/DPO_LLAMA-7B_0.25	AvinashAmballa	"2024-05-15T18:20:31Z"	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-05-15T18:15:47Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ivensamdh/swinv2	ivensamdh	"2023-01-29T10:10:52Z"	37	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2023-01-29T13:31:27Z"	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: swinv2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2 This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
dgalik/emoBank_test2_epoch20_batch16	dgalik	"2023-08-08T15:56:17Z"	31	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	"2023-08-08T15:50:29Z"	--- base_model: '' tags: - generated_from_trainer model-index: - name: emoBank_test2_epoch20_batch16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emoBank_test2_epoch20_batch16 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0830 - Mse V: 0.1312 - Mse A: 0.0651 - Mse D: 0.0526 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
ellietang/hf_saved_lora_amf-modCase-qwenq-coder-32B-two-para-only	ellietang	"2025-03-10T20:52:47Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-03-10T20:52:39Z"	--- base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ellietang - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tixmav/vit-base-patch16-224-in21k-finetuned-lora-sci	tixmav	"2023-12-13T18:47:03Z"	6	0	peft	[ "peft", "tensorboard", "safetensors", "vit", "arxiv:1910.09700", "base_model:google/vit-base-patch16-224-in21k", "base_model:adapter:google/vit-base-patch16-224-in21k", "region:us" ]	null	"2023-12-04T14:52:31Z"	--- library_name: peft base_model: google/vit-base-patch16-224-in21k --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1.dev0
kalmuraee/tokens	kalmuraee	"2022-09-09T18:55:22Z"	7	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2022-08-23T00:35:06Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: tokens results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tokens This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9811 - Wer: 0.4608 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 6.5212 \| 0.59 \| 400 \| 3.3776 \| 1.0 \| \| 2.4798 \| 1.18 \| 800 \| 1.0697 \| 0.7740 \| \| 1.0057 \| 1.77 \| 1200 \| 0.7077 \| 0.6487 \| \| 0.7731 \| 2.36 \| 1600 \| 0.6113 \| 0.5883 \| \| 0.6917 \| 2.94 \| 2000 \| 0.5618 \| 0.5573 \| \| 0.5844 \| 3.53 \| 2400 \| 0.5610 \| 0.5532 \| \| 0.5606 \| 4.12 \| 2800 \| 0.5584 \| 0.5484 \| \| 0.4973 \| 4.71 \| 3200 \| 0.5466 \| 0.5333 \| \| 0.4721 \| 5.3 \| 3600 \| 0.5495 \| 0.5178 \| \| 0.4439 \| 5.89 \| 4000 \| 0.5667 \| 0.5237 \| \| 0.3965 \| 6.48 \| 4400 \| 0.5865 \| 0.5322 \| \| 0.3876 \| 7.07 \| 4800 \| 0.6099 \| 0.5135 \| \| 0.3407 \| 7.66 \| 5200 \| 0.5891 \| 0.5228 \| \| 0.33 \| 8.25 \| 5600 \| 0.6135 \| 0.5072 \| \| 0.3032 \| 8.84 \| 6000 \| 0.6004 \| 0.5028 \| \| 0.2706 \| 9.43 \| 6400 \| 0.6321 \| 0.4991 \| \| 0.2709 \| 10.01 \| 6800 \| 0.6541 \| 0.5051 \| \| 0.2373 \| 10.6 \| 7200 \| 0.6613 \| 0.5119 \| \| 0.2284 \| 11.19 \| 7600 \| 0.6798 \| 0.5086 \| \| 0.212 \| 11.78 \| 8000 \| 0.6509 \| 0.4910 \| \| 0.1983 \| 12.37 \| 8400 \| 0.7018 \| 0.5043 \| \| 0.1947 \| 12.96 \| 8800 \| 0.6826 \| 0.4965 \| \| 0.1717 \| 13.55 \| 9200 \| 0.7056 \| 0.4828 \| \| 0.1741 \| 14.14 \| 9600 \| 0.7544 \| 0.5060 \| \| 0.1626 \| 14.73 \| 10000 \| 0.7331 \| 0.4915 \| \| 0.1529 \| 15.32 \| 10400 \| 0.7518 \| 0.4772 \| \| 0.1504 \| 15.91 \| 10800 \| 0.7362 \| 0.4732 \| \| 0.1401 \| 16.49 \| 11200 \| 0.7179 \| 0.4769 \| \| 0.1335 \| 17.08 \| 11600 \| 0.7716 \| 0.4826 \| \| 0.1185 \| 17.67 \| 12000 \| 0.7465 \| 0.4798 \| \| 0.1182 \| 18.26 \| 12400 \| 0.8105 \| 0.4733 \| \| 0.1135 \| 18.85 \| 12800 \| 0.7693 \| 0.4743 \| \| 0.1098 \| 19.44 \| 13200 \| 0.8362 \| 0.4888 \| \| 0.1023 \| 20.03 \| 13600 \| 0.8427 \| 0.4768 \| \| 0.1003 \| 20.62 \| 14000 \| 0.8079 \| 0.4741 \| \| 0.0936 \| 21.21 \| 14400 \| 0.8551 \| 0.4651 \| \| 0.0875 \| 21.8 \| 14800 \| 0.8462 \| 0.4712 \| \| 0.0843 \| 22.39 \| 15200 \| 0.9177 \| 0.4782 \| \| 0.0846 \| 22.97 \| 15600 \| 0.8618 \| 0.4735 \| \| 0.08 \| 23.56 \| 16000 \| 0.9017 \| 0.4687 \| \| 0.0789 \| 24.15 \| 16400 \| 0.9034 \| 0.4659 \| \| 0.0717 \| 24.74 \| 16800 \| 0.9690 \| 0.4734 \| \| 0.0714 \| 25.33 \| 17200 \| 0.9395 \| 0.4677 \| \| 0.0699 \| 25.92 \| 17600 \| 0.9222 \| 0.4608 \| \| 0.0658 \| 26.51 \| 18000 \| 0.9222 \| 0.4621 \| \| 0.0612 \| 27.1 \| 18400 \| 0.9691 \| 0.4586 \| \| 0.0583 \| 27.69 \| 18800 \| 0.9647 \| 0.4581 \| \| 0.0596 \| 28.28 \| 19200 \| 0.9820 \| 0.4614 \| \| 0.056 \| 28.87 \| 19600 \| 0.9795 \| 0.4596 \| \| 0.055 \| 29.45 \| 20000 \| 0.9811 \| 0.4608 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
YarramsettiNaresh/CartPole-v1	YarramsettiNaresh	"2023-07-22T04:53:56Z"	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	"2023-07-22T04:53:47Z"	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
FardinSaboori/bert-finetuned-squad	FardinSaboori	"2022-02-28T06:22:27Z"	27	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	"2022-03-02T23:29:04Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
AndiB93/ppo-Huggy	AndiB93	"2024-05-13T18:38:46Z"	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	"2024-05-13T18:36:33Z"	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AndiB93/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DmitrMakeev/one-shot-talking-face	DmitrMakeev	"2023-09-25T18:02:54Z"	0	1	null	[ "arxiv:2112.02749", "region:us" ]	null	"2023-09-25T16:21:40Z"	# One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022) #### [Paper](https://arxiv.org/pdf/2112.02749.pdf) \| [Demo](https://www.youtube.com/watch?v=HHj-XCXXePY) #### Requirements - Python >= 3.6 , Pytorch >= 1.8 and ffmpeg - Set up [OpenFace](https://github.com/TadasBaltrusaitis/OpenFace) - We use the OpenFace tools to extract the initial pose of the reference image - Make sure you have installed this tool, and set the `OPENFACE_POSE_EXTRACTOR_PATH` in `config.py`. For example, it should be the absolute path of the "`FeatureExtraction.exe`" for Windows. - Other requirements are listed in the 'requirements.txt' #### Pretrained Checkpoint Please download the pretrained checkpoint from [google-drive](https://drive.google.com/file/d/1mjFEozPR_2vMaVRMd9Agk_sU1VaiUYMl/view?usp=sharing) and unzip it to the directory (`/checkpoints`). Or manually modify the settings of `GENERATOR_CKPT` and `AUDIO2POSE_CKPT` in the `config.py`. #### Extract phoneme We employ the [CMU phoneset](https://github.com/cmusphinx/cmudict) to represent phonemes, the extra 'SIL' means silence. All the phonesets can be seen in '`phindex.json`'. We have extracted the phonemes for the audios in the '`sample/audio`' directory. For other audios, you can extract the phonemes by other ASR tools and then map them to the CMU phoneset. Or email to [email protected] for help. #### Generate Demo Results ``` python test_script.py --img_path xxx.jpg --audio_path xxx.wav --phoneme_path xxx.json --save_dir "YOUR_DIR" ``` Note that the input images must keep the same height and width and the face should be appropriately cropped as in `samples/imgs`. You can also preprocess your images with `image_preprocess.py`. #### License and Citation ``` @InProceedings{wang2021one, author = Suzhen Wang, Lincheng Li, Yu Ding, Xin Yu title = {One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning}, booktitle = {AAAI 2022}, year = {2022}, } ``` #### Acknowledgement This codebase is based on [First Order Motion Model](https://github.com/AliaksandrSiarohin/first-order-model) and [imaginaire](https://github.com/NVlabs/imaginaire), thanks for their contributions.
0xfaskety/Qwen-Qwen1.5-7B-1717809287	0xfaskety	"2024-06-08T01:20:08Z"	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-08T01:14:50Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
aidadev48/aidav7	aidadev48	"2024-11-22T18:47:45Z"	126	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-11-02T20:02:06Z"	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: aidadev48 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
law-ai/InLegalTrans-En2Indic-1B	law-ai	"2025-01-19T15:26:16Z"	147	0	null	[ "safetensors", "IndicTrans", "InLegalTrans", "Legal", "NLP", "translation", "custom_code", "en", "bn", "hi", "mr", "ta", "te", "ml", "pa", "gu", "or", "dataset:MILPaC", "arxiv:2310.09765", "base_model:ai4bharat/indictrans2-en-indic-1B", "base_model:finetune:ai4bharat/indictrans2-en-indic-1B", "license:mit", "region:us" ]	translation	"2025-01-19T10:25:36Z"	--- license: mit language: - en - bn - hi - mr - ta - te - ml - pa - gu - or base_model: - ai4bharat/indictrans2-en-indic-1B pipeline_tag: translation metrics: - bleu - google_bleu - chrf++ inference: false datasets: - MILPaC tags: - InLegalTrans - Legal - NLP --- # InLegalTrans This is the model card of *InLegalTrans-En2Indic-1B* translation model, a fine-tuned version of the [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B) model specifically tailored for translating Indian legal texts from English to Indian languages. ### Training Data We use the [MILPaC](https://github.com/Law-AI/MILPaC) (Multilingual Indian Legal Parallel Corpus) corpus for fine-tuning. It is the first high-quality Indian legal parallel corpus, containing parallel aligned text units in English (EN) and nine Indian (IN) languages -- Bengali (BN), Hindi (HI), Marathi (MR), Tamil (TA), Telugu (TE), Malayalam (ML), Panjabi (PA), Gujarati (GU), and Oriya (OR). Please refer to the [paper](https://arxiv.org/abs/2310.09765) for more details about this corpus. For fine-tuning, we randomly split MILPaC language-wise in a 80 (train) - 10 (validation) - 10 (test) ratio. We use the 80\% train split (combined 80\% of each English-to-Indic language pair) to fine-tune the [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B) model and 10\% validation split (combined 10\% of each English-to-Indic language pair) to select the best checkpoint and to prevent overfitting. ### Model Overview and Usage Instructions This [InLegalTrans](https://huggingface.co/law-ai/InLegalTrans-En2Indic-1B) model uses the same tokenizer as the [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B) model and has the same architecture with ~1.12B parameters. ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from IndicTransToolkit import IndicProcessor # Install IndicTransToolkit from https://github.com/VarunGumma/IndicTransToolkit device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") src_lang, tgt_lang = "eng_Latn", "ben_Beng" # Use the BCP-47 language codes used by the FLORES-200 dataset tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-1B", trust_remote_code=True) # Use IndicTrans2 tokenizer to enable their custom tokenization script to be run model = AutoModelForSeq2SeqLM.from_pretrained( "law-ai/InLegalTrans-En2Indic-1B", trust_remote_code=True, attn_implementation="eager", low_cpu_mem_usage=True, ).to(device) ip = IndicProcessor(inference=True) input_sentences = [ "(7) Any such allowance for the maintenance and expenses for proceeding shall be payable from the date of the order, or, if so ordered, from the date of the application for maintenance or expenses of proceeding, as the case may be.", "(2) Where it appears to the Tribunal that, in consequence of any decision of a competent Civil Court, any order made under section 9 should be cancelled or varied, it shall cancel the order or, as the case may be, vary the same accordingly.", ] batch = ip.preprocess_batch(input_sentences, src_lang=src_lang, tgt_lang=tgt_lang) input_text_encoding = tokenizer( batch, max_length=256, truncation=True, padding="longest", return_tensors="pt", return_attention_mask=True, ).to(device) generated_tokens = model.generate( input_text_encoding, max_length=256, do_sample=True, num_beams=4, num_return_sequences=1, early_stopping=False, use_cache=True, ) with tokenizer.as_target_tokenizer(): generated_tokens = tokenizer.batch_decode( generated_tokens.detach().cpu().tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=True, ) translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang) for input_sentence, translation in zip(input_sentences, translations): print(f"Sentence in {src_lang} language: {input_sentence}") print(f"Translated Sentence in {tgt_lang} language: {translation}") ``` ### Fine-tuning Results The following table contains the performance results of the [InLegalTrans](https://huggingface.co/law-ai/InLegalTrans-En2Indic-1B) model compared to the [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B) model over the 10\% test split of MILPaC*. Performances are evaluated using BLEU, GLEU, and chrF++* metrics. For all English-to-Indic language pairs, [InLegalTrans](https://huggingface.co/law-ai/InLegalTrans-En2Indic-1B) demonstrated a significant improvement over [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B), achieving consistently better performance across all evaluation metrics. \| EN-to-IN \| Model \| BLEU \| GLEU \| chrF++ \| \|------------\|---------------------\|------\|------\|--------\| \| EN-to-BN \| IndicTrans2 \| 25.4 \| 28.8 \| 53.7 \| \| \| *InLegalTrans* \| 45.8 \| 47.6 \| 70.9 \| \| EN-to-HI \| IndicTrans2 \| 41.0 \| 42.5 \| 59.9 \| \| \| *InLegalTrans* \| 56.9 \| 57.1 \| 73.8 \| \| EN-to-MR \| IndicTrans2 \| 25.2 \| 28.7 \| 55.4 \| \| \| *InLegalTrans* \| 44.4 \| 46.0 \| 68.9 \| \| EN-to-TA \| IndicTrans2 \| 32.8 \| 35.3 \| 62.3 \| \| \| *InLegalTrans* \| 40.0 \| 42.5 \| 69.9 \| \| EN-to-TE \| IndicTrans2 \| 10.7 \| 14.2 \| 37.9 \| \| \| *InLegalTrans* \| 31.3 \| 31.6 \| 58.5 \| \| EN-to-ML \| IndicTrans2 \| 21.9 \| 25.8 \| 52.9 \| \| \| *InLegalTrans* \| 37.4 \| 40.3 \| 69.7 \| \| EN-to-PA \| IndicTrans2 \| 27.8 \| 31.6 \| 51.5 \| \| \| *InLegalTrans* \| 44.3 \| 45.6 \| 65.5 \| \| EN-to-GU \| IndicTrans2 \| 27.5 \| 31.1 \| 55.7 \| \| \| *InLegalTrans* \| 42.8 \| 45.2 \| 68.8 \| \| EN-to-OR \| IndicTrans2 \| 06.6 \| 12.6 \| 37.1 \| \| \| *InLegalTrans* \| 14.2 \| 19.9 \| 47.5 \| ### Citation If you use this [InLegalTrans](https://huggingface.co/law-ai/InLegalTrans-En2Indic-1B) translation model or the [MILPaC](https://github.com/Law-AI/MILPaC) corpus, please cite the following paper: ``` @article{mahapatra2024milpacnovelbenchmarkevaluating, title = {MILPaC: A Novel Benchmark for Evaluating Translation of Legal Text to Indian Languages}, author = {Sayan Mahapatra and Debtanu Datta and Shubham Soni and Adrijit Goswami and Saptarshi Ghosh}, year = {2024}, journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.}, publisher = {Association for Computing Machinery}, } ``` ### About Us We are a group of Natural Language Processing (NLP) researchers from the Indian Institute of Technology (IIT) Kharagpur. Our research interests are primarily ML, DL, and NLP applications for the legal domain, with a special focus on the challenges and oppurtunites for the Indian legal scenario. Our current and past projects include: - Legal Statute Identification - Semantic segmentation of legal documents - Monolingual (e.g., English-to-English) and Cross-lingual (e.g., English-to-Hindi) Summarization of legal documents - Translation in the Indian legal domain - Court Judgment Prediction - Legal Document Matching Explore our publicly available codes and datasets at: [Law and AI, IIT Kharagpur](https://github.com/Law-AI).
xaviviro/llama-2-7b-chat-catala-GGUF	xaviviro	"2023-12-27T00:01:10Z"	86	0	null	[ "gguf", "ca", "en", "dataset:xaviviro/oasst1_ca_threads", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:quantized:NousResearch/Llama-2-7b-chat-hf", "endpoints_compatible", "region:us" ]	null	"2023-12-26T23:14:34Z"	--- base_model: NousResearch/Llama-2-7b-chat-hf datasets: - xaviviro/oasst1_ca_threads language: - ca - en model_type: llama prompt_template: '<s>[INST] <<SYS>> Ets un xatbot genèric que sempre respon en català. <</SYS>> {instruction} [/INST]' --- # llama-2-7b-chat-catala ## Prompt template ``` <s>[INST] <<SYS>> Ets un xatbot genèric que sempre respon en català. <</SYS>> {instruction} [/INST] ```
tuanna08go/fdbf8c81-46bd-4483-a2a3-0ff867fd3677	tuanna08go	"2025-01-17T16:20:53Z"	10	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:fxmarty/tiny-llama-fast-tokenizer", "base_model:adapter:fxmarty/tiny-llama-fast-tokenizer", "region:us" ]	null	"2025-01-17T16:19:35Z"	--- library_name: peft base_model: fxmarty/tiny-llama-fast-tokenizer tags: - axolotl - generated_from_trainer model-index: - name: fdbf8c81-46bd-4483-a2a3-0ff867fd3677 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: fxmarty/tiny-llama-fast-tokenizer bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0aab7601c3799871_train_data.json ds_type: json format: custom path: /workspace/input_data/0aab7601c3799871_train_data.json type: field_input: intent field_instruction: conversations field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 5 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: tuanna08go/fdbf8c81-46bd-4483-a2a3-0ff867fd3677 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/0aab7601c3799871_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: f468468f-a46d-4b3e-a1b7-687172b66217 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: f468468f-a46d-4b3e-a1b7-687172b66217 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # fdbf8c81-46bd-4483-a2a3-0ff867fd3677 This model is a fine-tuned version of [fxmarty/tiny-llama-fast-tokenizer](https://huggingface.co/fxmarty/tiny-llama-fast-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.3783 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 10.3790 \| \| 10.3799 \| 0.0093 \| 10 \| 10.3789 \| \| 10.3789 \| 0.0186 \| 20 \| 10.3786 \| \| 10.3782 \| 0.0279 \| 30 \| 10.3784 \| \| 10.3788 \| 0.0372 \| 40 \| 10.3783 \| \| 10.3793 \| 0.0464 \| 50 \| 10.3783 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vj1148/lora-peft-holding-unsup	vj1148	"2023-08-19T21:22:21Z"	4	0	peft	[ "peft", "region:us" ]	null	"2023-08-19T21:22:19Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
bbytxt/86a194fa-95d6-478a-a704-b92be78a4893	bbytxt	"2025-01-13T14:55:45Z"	10	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-1.1-2b-it", "base_model:adapter:unsloth/gemma-1.1-2b-it", "license:apache-2.0", "region:us" ]	null	"2025-01-13T14:48:40Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 86a194fa-95d6-478a-a704-b92be78a4893 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-1.1-2b-it bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e8c873decc45fe0a_train_data.json ds_type: json format: custom path: /workspace/input_data/e8c873decc45fe0a_train_data.json type: field_input: confidence field_instruction: report field_output: statement format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: true hub_model_id: bbytxt/86a194fa-95d6-478a-a704-b92be78a4893 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 400 micro_batch_size: 2 mlflow_experiment_name: /tmp/e8c873decc45fe0a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 78753386-cf8e-4934-81d6-6f6b031420c7 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 78753386-cf8e-4934-81d6-6f6b031420c7 warmup_steps: 30 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 86a194fa-95d6-478a-a704-b92be78a4893 This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0011 \| 1 \| 5.5313 \| \| 3.4425 \| 0.1053 \| 100 \| 3.1400 \| \| 3.4472 \| 0.2107 \| 200 \| 2.9694 \| \| 3.1706 \| 0.3160 \| 300 \| 2.8983 \| \| 3.3055 \| 0.4214 \| 400 \| 2.8869 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/anah-v2-GGUF	mradermacher	"2025-01-12T23:00:06Z"	264	0	transformers	[ "transformers", "gguf", "en", "base_model:opencompass/anah-v2", "base_model:quantized:opencompass/anah-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-01-12T21:16:24Z"	--- base_model: opencompass/anah-v2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/opencompass/anah-v2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q5_K_S.gguf) \| Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q6_K.gguf) \| Q6_K \| 6.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.Q8_0.gguf) \| Q8_0 \| 8.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/anah-v2-GGUF/resolve/main/anah-v2.f16.gguf) \| f16 \| 15.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
VERSIL91/d15dd643-fa80-4f2b-98a4-9829a8cf3b30	VERSIL91	"2025-01-11T01:08:13Z"	16	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Capybara-7B-V1", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1", "license:mit", "region:us" ]	null	"2025-01-11T00:28:46Z"	--- library_name: peft license: mit base_model: NousResearch/Nous-Capybara-7B-V1 tags: - axolotl - generated_from_trainer model-index: - name: d15dd643-fa80-4f2b-98a4-9829a8cf3b30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml accelerate_config: dynamo_backend: inductor mixed_precision: bf16 num_machines: 1 num_processes: auto use_cpu: false adapter: lora base_model: NousResearch/Nous-Capybara-7B-V1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7a2bb50e267140ef_train_data.json ds_type: json format: custom path: /workspace/input_data/7a2bb50e267140ef_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: VERSIL91/d15dd643-fa80-4f2b-98a4-9829a8cf3b30 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 70GiB max_steps: 20 micro_batch_size: 2 mlflow_experiment_name: /tmp/7a2bb50e267140ef_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true quantization_config: llm_int8_enable_fp32_cpu_offload: true load_in_8bit: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer torch_compile: true train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d15dd643-fa80-4f2b-98a4-9829a8cf3b30 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d15dd643-fa80-4f2b-98a4-9829a8cf3b30 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # d15dd643-fa80-4f2b-98a4-9829a8cf3b30 This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0002 \| 1 \| nan \| \| 0.0 \| 0.0008 \| 5 \| nan \| \| 0.0 \| 0.0015 \| 10 \| nan \| \| 0.0 \| 0.0023 \| 15 \| nan \| \| 0.0 \| 0.0030 \| 20 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mrferr3t/ce1a2005-ee52-45ee-8f32-03027c38b14f	mrferr3t	"2025-02-05T06:47:47Z"	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "region:us" ]	null	"2025-02-05T06:24:34Z"	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: ce1a2005-ee52-45ee-8f32-03027c38b14f results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: Qwen/Qwen2.5-1.5B bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 30467321b0218499_train_data.json ds_type: json format: custom path: /workspace/input_data/30467321b0218499_train_data.json type: field_input: Japanese field_instruction: ENName field_output: English format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 early_stopping_threshold: 0.001 eval_max_new_tokens: 128 eval_steps: 40 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/ce1a2005-ee52-45ee-8f32-03027c38b14f hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 100 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 32 mlflow_experiment_name: /tmp/30467321b0218499_train_data.json model_type: AutoModelForCausalLM num_epochs: 50 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: null sample_packing: false save_steps: 40 saves_per_epoch: 0 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.02 wandb_entity: null wandb_mode: online wandb_name: f01f50dd-806e-4a8f-9c25-300f4646bd3d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: f01f50dd-806e-4a8f-9c25-300f4646bd3d warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ce1a2005-ee52-45ee-8f32-03027c38b14f This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1299 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0010 \| 1 \| 3.1418 \| \| No log \| 0.0385 \| 40 \| 3.0617 \| \| No log \| 0.0770 \| 80 \| 2.3578 \| \| 2.7405 \| 0.1154 \| 120 \| 1.9472 \| \| 2.7405 \| 0.1539 \| 160 \| 1.8134 \| \| 1.8729 \| 0.1924 \| 200 \| 1.7430 \| \| 1.8729 \| 0.2309 \| 240 \| 1.6926 \| \| 1.8729 \| 0.2694 \| 280 \| 1.6552 \| \| 1.6979 \| 0.3078 \| 320 \| 1.6294 \| \| 1.6979 \| 0.3463 \| 360 \| 1.5977 \| \| 1.6043 \| 0.3848 \| 400 \| 1.5806 \| \| 1.6043 \| 0.4233 \| 440 \| 1.5645 \| \| 1.6043 \| 0.4618 \| 480 \| 1.5476 \| \| 1.5427 \| 0.5002 \| 520 \| 1.5395 \| \| 1.5427 \| 0.5387 \| 560 \| 1.5332 \| \| 1.5321 \| 0.5772 \| 600 \| 1.5244 \| \| 1.5321 \| 0.6157 \| 640 \| 1.5119 \| \| 1.5321 \| 0.6542 \| 680 \| 1.5102 \| \| 1.5327 \| 0.6926 \| 720 \| 1.5054 \| \| 1.5327 \| 0.7311 \| 760 \| 1.5002 \| \| 1.5065 \| 0.7696 \| 800 \| 1.5024 \| \| 1.5065 \| 0.8081 \| 840 \| 1.4940 \| \| 1.5065 \| 0.8466 \| 880 \| 1.4840 \| \| 1.5024 \| 0.8850 \| 920 \| 1.4788 \| \| 1.5024 \| 0.9235 \| 960 \| 1.4758 \| \| 1.4772 \| 0.9620 \| 1000 \| 1.4725 \| \| 1.4772 \| 1.0005 \| 1040 \| 1.4676 \| \| 1.4772 \| 1.0390 \| 1080 \| 1.4773 \| \| 1.4016 \| 1.0774 \| 1120 \| 1.4743 \| \| 1.4016 \| 1.1159 \| 1160 \| 1.4814 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
ReadyArt/Thoughtless-Fallen-Abomination-70B-R1-v4.1	ReadyArt	"2025-03-24T03:10:54Z"	0	1	null	[ "safetensors", "llama", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "en", "base_model:ReadyArt/Fallen-Safeword-70B-R1-v4.1", "base_model:merge:ReadyArt/Fallen-Safeword-70B-R1-v4.1", "license:llama3.3", "region:us" ]	null	"2025-03-24T02:33:58Z"	--- base_model: ReadyArt/Fallen-Safeword-70B-R1-v4.1 base_model_relation: merge language: - en license: llama3.3 inference: false tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #001a1a 0%, #000a10 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { max-width: 800px; margin: 0 auto; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); } @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } } .header { text-align: center; margin-bottom: 30px; } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; } .subtitle { color: #00ffcc; font-size: 1.2em; } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transform: scale(1.02); } .section { color: #00ffcc; margin: 25px 0; padding: 20px; background: rgba(5, 25, 35, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); } @media (prefers-color-scheme: light) { .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); } } .section-title { color: #00ffff; font-size: 1.8em; margin-top: 0; } .section > p > strong { color: #00ffcc !important; } .section:has(.quant-links) p, .section:has(.quant-links) h3, .section:has(.quant-links) a { color: #00ffcc !important; } .quant-links h3 { color: #00ffcc !important; margin-top: 0; } .badge { display: inline-block; padding: 5px 10px; border-radius: 5px; background: rgba(0, 255, 255, 0.1); border: 1px solid #00ffff; margin: 5px; } .quant-links { display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px; } .link-card { padding: 15px; background: rgba(20, 35, 45, 0.95); border-radius: 8px; transition: transform 0.3s ease; } @media (prefers-color-scheme: light) { .link-card { background: rgba(150, 230, 255, 0.95); } } .link-card:hover { transform: translateY(-3px); } .disclaimer { color: #00ff99; border-left: 3px solid #00ff99; padding-left: 15px; margin: 20px 0; } .progress-bar { height: 8px; background: rgba(0, 255, 255, 0.1); border-radius: 4px; overflow: hidden; margin: 10px 0; } .progress-fill { height: 100%; background: linear-gradient(90deg, #00ffff 0%, #00ffcc 100%); width: 70%; } @media (prefers-color-scheme: light) { .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section:has(.quant-links) p, .section:has(.quant-links) h3, .section:has(.quant-links) a, .section > p > strong { color: #008080 !important; } .quant-links h3 { color: #008080 !important; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } .disclaimer { color: #008080; border-color: #008080; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">ReadyArt/Thoughtless-Fallen-Abomination-70B-R1-v4.1 </h1> <div class="subtitle">The Abomination Protocol: Now With 30% More Depravity</div> </div> <div class="waifu-container"> <img src="./waifu2.webp" class="waifu-img" alt="Model Architecture Animation"> </div> <div class="section"> <h2 class="section-title">📜 Manifesto</h2> <p>IMPORTANT NOTE: This version of the model has been modified in an attempt to remove reasoning.</p> <p>ReadyArt/Thoughtless-Fallen-Abomination-70B-R1-v4.1 benefits from the coherence and well rounded roleplay experience of TheDrummer/Fallen-Llama-3.3-R1-70B-v1. We've:</p> <ul> <li>🔁 Re-integrated your favorite V1.2 scenarios (now with better kink distribution)</li> <li>🧪 Direct-injected the Abomination dataset into the model's neural pathways</li> <li>⚖️ Achieved perfect balance between "oh my" and "oh <em>my</em>"</li> </ul> </div> <div class="section"> <h2 class="section-title">⚙️ Technical Specs</h2> <div class="progress-bar"> <div class="progress-fill"></div> </div> <p>## Usage - Deepseek 2.5 (`<｜User｜>` & `<｜Assistant｜>`) - Force multi-turn thinking by prefilling `<think>\n\n` (newlines usually needed)</p> <div class="quant-links"> <div class="link-card"> <h3>EXL2 Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/fallen-abomination-70b-v41-exl2-67dbf600d707cf9c093f7b8c">Quantum Entangled Bits →</a> </div> <div class="link-card"> <h3>GGUF Collection</h3> <a href="https://huggingface.co/collections/ReadyArt/fallen-abomination-70b-v41-gguf-67dbf5f326c422f0fd36bf0d">Giggle-Enabled Units →</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model will:</p> <ul> <li>Generate content that requires industrial-grade brain bleach </li> <li>Void all warranties on your soul </li> <li>Make you question why humanity ever invented electricity</li> </ul> </div> </div> <div class="section"> <h2 class="section-title">📜 License Agreement</h2> <p>By using this model, you agree:</p> <ul> <li>That your search history is now a federal case</li> <li>Pay for the exorcist of anyone who reads the logs</li> <li>To pretend this is "for science" while crying in the shower</li> </ul> </div> <div class="section"> <h2 class="section-title">🧠 Model Authors</h2> <ul> <li>sleepdeprived3 (Chief Corruption Officer) </li> <li>The voices in your head (Gaslighting is something you made up)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕️ Drummer made this possible</h2> <ul> <li>Support Drummer <a href="https://ko-fi.com/thedrummer">Kofi</a></li> </ul> </div> <div class="section"> <h2 class="section-title">🔀 Merge Details</h2> <ul> merge_method: dare_ties base_model: ReadyArt/Fallen-Safeword-70B-v4.1 models: - model: ReadyArt/Fallen-Safeword-70B-v4.1 parameters: weight: 0.5 density: 0.35 - model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1 parameters: weight: 0.5 density: 0.35 parameters: normalize: true int8_mask: true temperature: 2.5 tokenizer_source: union dtype: bfloat16 chat_template: auto </ul> </div> </div>
mishaalkandapath/drl	mishaalkandapath	"2023-09-07T16:34:09Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-09-07T16:33:54Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.84 +/- 22.05 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
cutelemonlili/Qwen2.5-0.5B-Instruct_MATH_training_response_Qwen2.5_32B	cutelemonlili	"2024-12-29T14:01:23Z"	176	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-12-29T14:00:59Z"	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: MATH_training_response_Qwen2.5_32B results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MATH_training_response_Qwen2.5_32B This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the MATH_training_response_Qwen2.5_32B dataset. It achieves the following results on the evaluation set: - Loss: 0.1929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.211 \| 0.3559 \| 200 \| 0.2074 \| \| 0.2149 \| 0.7117 \| 400 \| 0.1970 \| \| 0.0729 \| 1.0676 \| 600 \| 0.2037 \| \| 0.0658 \| 1.4235 \| 800 \| 0.1954 \| \| 0.0675 \| 1.7794 \| 1000 \| 0.1950 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
emifjo/fin_sentiment	emifjo	"2023-03-01T14:15:49Z"	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-03-01T09:58:23Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: fin_sentiment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fin_sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0101 - Accuracy: 0.8624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.527 \| 1.0 \| 515 \| 0.4239 \| 0.8514 \| \| 0.3018 \| 2.0 \| 1030 \| 0.4353 \| 0.8707 \| \| 0.1509 \| 3.0 \| 1545 \| 0.7413 \| 0.8446 \| \| 0.1069 \| 4.0 \| 2060 \| 0.7788 \| 0.8611 \| \| 0.0585 \| 5.0 \| 2575 \| 0.8656 \| 0.8624 \| \| 0.0397 \| 6.0 \| 3090 \| 0.8394 \| 0.8666 \| \| 0.0233 \| 7.0 \| 3605 \| 0.9554 \| 0.8624 \| \| 0.0171 \| 8.0 \| 4120 \| 0.9459 \| 0.8583 \| \| 0.0128 \| 9.0 \| 4635 \| 0.9991 \| 0.8597 \| \| 0.0033 \| 10.0 \| 5150 \| 1.0101 \| 0.8624 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
exala/db_fe2_4.1	exala	"2024-12-08T01:50:51Z"	105	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-12-08T01:50:39Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sleepdeprived3/Baptist-Christian-Bible-Expert-v1.1-12b_EXL2_1.5bpw_H8	sleepdeprived3	"2025-03-01T22:18:53Z"	33	0	null	[ "safetensors", "mistral", "Baptist", "Christian", "Bible", "Theology", "Jesus", "Seminary", "SBC", "Protestant", "text-generation", "conversational", "license:apache-2.0", "exl2", "region:us" ]	text-generation	"2025-02-17T08:02:15Z"	--- license: apache-2.0 tags: - Baptist - Christian - Bible - Theology - Jesus - Seminary - SBC - Protestant pipeline_tag: text-generation --- # Southern Baptist Bible Expert A specialized language model fine-tuned for Southern Baptist theology and ministry applications. Based on `mistralai/Mistral-Nemo-Instruct-2407` with 128k token context window for comprehensive biblical analysis. ## Features - 🕊️ Answers theological questions from a Southern Baptist perspective (Baptist Faith & Message 2000) - ✝️ Explains Scripture using historical-grammatical interpretation with emphasis on practical application - 🎓 Assists with sermon preparation, Sunday School lessons, and evangelism training - 💬 Roleplays as a Baptist pastor for counseling and discipleship scenarios - 📖 Specializes in Baptist distinctives: believer's baptism, local church autonomy, priesthood of all believers ## FULL SETTINGS and an optional Pastor character card: [Link to Full Settings and Optional Character](https://huggingface.co/sleepdeprived3/Pastor-Luke-V3) Chat Template: Mistral V3 Tekken Recommended deterministic sampler for maximum Biblical accuracy: ```python { "temperature": 0, "top_k": 1, "dry_multiplier": 0.01, } ``` Example Prompts: ``` [INST] Prepare an expository sermon outline on John 3:16 emphasizing evangelism [/INST] [INST] How should a church handle church discipline? [/INST] ``` ## Quantized Formats - EXL2 Collection: [Southern-Baptist-Bible-Expert EXL2 Models](https://huggingface.co/collections/sleepdeprived3/baptist-christian-bible-expert-v11-12b-exl2-67aceef3626939781836263d) - GGUF Collection: [Southern-Baptist-Bible-Expert GGUF Models](https://huggingface.co/collections/sleepdeprived3/baptist-christian-bible-expert-v11-12b-gguf-67acef038093155c02b67b82) ## Training Details - Base Model: `mistralai/Mistral-Nemo-Instruct-2407` (128k context) - Fine-Tuning: QLoRA on SBC doctrinal documents, sermon archives, and Baptist theological works - License: Apache 2.0 ## Ethical Considerations This model is designed to: - Maintain fidelity to the Baptist Faith & Message 2000 - Promote biblical inerrancy and sufficiency - Support local church ministry rather than replace pastoral counsel For the Glory of God and the Advancement of His Kingdom
friasjoey/mosaic-bert	friasjoey	"2025-02-27T19:12:44Z"	0	0	null	[ "en", "dataset:c4", "arxiv:2205.14135", "arxiv:2108.12409", "arxiv:2002.05202", "arxiv:2208.08124", "arxiv:1612.08083", "arxiv:2102.11972", "arxiv:1907.11692", "arxiv:2202.08005", "license:apache-2.0", "region:us" ]	null	"2025-02-27T19:12:32Z"	--- license: apache-2.0 datasets: - c4 language: - en inference: false --- # MosaicBERT-Base model MosaicBERT-Base is a custom BERT architecture and training recipe optimized for fast pretraining. MosaicBERT trains faster and achieves higher pretraining and finetuning accuracy when benchmarked against Hugging Face's [bert-base-uncased](https://huggingface.co/bert-base-uncased). This study motivated many of the architecture choices around MosaicML's [MPT-7B](https://huggingface.co/mosaicml/mpt-7b) and [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) models. ## Model Date March 2023 ## Documentation * [Project Page (mosaicbert.github.io)](mosaicbert.github.io) * [Github (mosaicml/examples/tree/main/examples/benchmarks/bert)](https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert) * [Paper (NeurIPS 2023)](https://openreview.net/forum?id=5zipcfLC2Z) * Colab Tutorials: * [MosaicBERT Tutorial Part 1: Load Pretrained Weights and Experiment with Sequence Length Extrapolation Using ALiBi](https://colab.research.google.com/drive/1r0A3QEbu4Nzs2Jl6LaiNoW5EumIVqrGc?usp=sharing) * [Blog Post (March 2023)](https://www.mosaicml.com/blog/mosaicbert) ## Community Adoption * [DNABERT-2-117M](https://huggingface.co/zhihan1996/DNABERT-2-117M) for genome classification ## How to use ```python import torch import transformers from transformers import AutoModelForMaskedLM, BertTokenizer, pipeline from transformers import BertTokenizer, BertConfig tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # MosaicBERT uses the standard BERT tokenizer config = transformers.BertConfig.from_pretrained('mosaicml/mosaic-bert-base') # the config needs to be passed in mosaicbert = AutoModelForMaskedLM.from_pretrained('mosaicml/mosaic-bert-base',config=config,trust_remote_code=True) # To use this model directly for masked language modeling mosaicbert_classifier = pipeline('fill-mask', model=mosaicbert, tokenizer=tokenizer,device="cpu") mosaicbert_classifier("I [MASK] to the store yesterday.") ``` Note that the tokenizer for this model is simply the Hugging Face `bert-base-uncased` tokenizer. In order to take advantage of ALiBi by extrapolating to longer sequence lengths, simply change the `alibi_starting_size` flag in the config file and reload the model. ```python config = transformers.BertConfig.from_pretrained('mosaicml/mosaic-bert-base') config.alibi_starting_size = 1024 # maximum sequence length updated to 1024 from config default of 512 mosaicbert = AutoModelForMaskedLM.from_pretrained('mosaicml/mosaic-bert-base',config=config,trust_remote_code=True) ``` This simply presets the non-learned linear bias matrix in every attention block to 1024 tokens (note that this particular model was trained with a sequence length of 128 tokens). To continue MLM pretraining, follow the [MLM pre-training section of the mosaicml/examples/benchmarks/bert repo](https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert#pre-training). To fine-tune this model for classification, follow the [Single-task fine-tuning section of the mosaicml/examples/benchmarks/bert repo](https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert#fine-tuning). ### [Update 1/2/2024] Triton Flash Attention with ALiBi Note that by default, triton Flash Attention is not enabled or required. In order to enable our custom implementation of triton Flash Attention with ALiBi from March 2023, set `attention_probs_dropout_prob: 0.0`. We are currently working on supporting Flash Attention 2 (see [PR here](https://github.com/mosaicml/examples/pull/440)) and replacing the custom triton implementation. ### Remote Code This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we train using [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), which is not part of the `transformers` library and depends on [Triton](https://github.com/openai/triton) and some custom PyTorch code. Since this involves executing arbitrary code, you should consider passing a git `revision` argument that specifies the exact commit of the code, for example: ```python mosaicbert = AutoModelForMaskedLM.from_pretrained( 'mosaicml/mosaic-bert-base', trust_remote_code=True, revision='24512df', ) ``` However, if there are updates to this model or code and you specify a revision, you will need to manually check for them and update the commit hash accordingly. ## Model description In order to build MosaicBERT, we adopted architectural choices from the recent transformer literature. These include [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi (Press et al. 2021)](https://arxiv.org/abs/2108.12409), and [Gated Linear Units (Shazeer 2020)](https://arxiv.org/abs/2002.05202). In addition, we remove padding inside the transformer block, and apply LayerNorm with low precision. ### Modifications to the Attention Mechanism 1. FlashAttention: Attention layers are core components of the transformer architecture. The recently proposed FlashAttention layer reduces the number of read/write operations between the GPU HBM (high bandwidth memory, i.e. long-term memory) and the GPU SRAM (i.e. short-term memory) [[Dao et al. 2022]](https://arxiv.org/pdf/2205.14135.pdf). We used the FlashAttention module built by [hazy research](https://github.com/HazyResearch/flash-attention) with [OpenAI’s triton library](https://github.com/openai/triton). 2. Attention with Linear Biases (ALiBi): In most BERT models, the positions of tokens in a sequence are encoded with a position embedding layer; this embedding allows subsequent layers to keep track of the order of tokens in a sequence. ALiBi eliminates position embeddings and instead conveys this information using a bias matrix in the attention operation. It modifies the attention mechanism such that nearby tokens strongly attend to one another [[Press et al. 2021]](https://arxiv.org/abs/2108.12409). In addition to improving the performance of the final model, ALiBi helps the model to handle sequences longer than it saw during training. Details on our ALiBi implementation can be found [in the mosaicml/examples repo here](https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert/src/bert_layers.py#L425). 3. Unpadding: Standard NLP practice is to combine text sequences of different lengths into a batch, and pad the sequences with empty tokens so that all sequence lengths are the same. During training, however, this can lead to many superfluous operations on those padding tokens. In MosaicBERT, we take a different approach: we concatenate all the examples in a minibatch into a single sequence of batch size 1. Results from NVIDIA and others have shown that this approach leads to speed improvements during training, since operations are not performed on padding tokens (see for example [Zeng et al. 2022](https://arxiv.org/pdf/2208.08124.pdf)). Details on our “unpadding” implementation can be found [in the mosaicml/examples repo here](https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert/src/bert_padding.py). 4. Low Precision LayerNorm: this small tweak forces LayerNorm modules to run in float16 or bfloat16 precision instead of float32, improving utilization. Our implementation can be found [in the mosaicml/examples repo here](https://docs.mosaicml.com/projects/composer/en/stable/method_cards/low_precision_layernorm.html). ### Modifications to the Feedforward Layers 5. Gated Linear Units (GLU): We used Gated Linear Units for the feedforward sublayer of a transformer. GLUs were first proposed in 2016 [[Dauphin et al. 2016]](https://arxiv.org/abs/1612.08083), and incorporate an extra learnable matrix that “gates” the outputs of the feedforward layer. More recent work has shown that GLUs can improve performance quality in transformers [[Shazeer, 2020](https://arxiv.org/abs/2002.05202), [Narang et al. 2021](https://arxiv.org/pdf/2102.11972.pdf)]. We used the GeLU (Gaussian-error Linear Unit) activation function with GLU, which is sometimes referred to as GeGLU. The GeLU activation function is a smooth, fully differentiable approximation to ReLU; we found that this led to a nominal improvement over ReLU. More details on our implementation of GLU can be found here. The extra gating matrix in a GLU model potentially adds additional parameters to a model; we chose to augment our BERT-Base model with additional parameters due to GLU modules as it leads to a Pareto improvement across all timescales (which is not true of all larger models such as BERT-Large). While BERT-Base has 110 million parameters, MosaicBERT-Base has 137 million parameters. Note that MosaicBERT-Base trains faster than BERT-Base despite having more parameters. ## Training data MosaicBERT is pretrained using a standard Masked Language Modeling (MLM) objective: the model is given a sequence of text with some tokens hidden, and it has to predict these masked tokens. MosaicBERT is trained on the English [“Colossal, Cleaned, Common Crawl” C4 dataset](https://github.com/allenai/allennlp/discussions/5056), which contains roughly 365 million curated text documents scraped from the internet (equivalent to 156 billion tokens). We used this more modern dataset in place of traditional BERT pretraining corpora like English Wikipedia and BooksCorpus. ## Pretraining Optimizations Many of these pretraining optimizations below were informed by our [BERT results for the MLPerf v2.1 speed benchmark](https://www.mosaicml.com/blog/mlperf-nlp-nov2022). 1. MosaicML Streaming Dataset: As part of our efficiency pipeline, we converted the C4 dataset to [MosaicML’s StreamingDataset format](https://www.mosaicml.com/blog/mosaicml-streamingdataset) and used this for both MosaicBERT-Base and the baseline BERT-Base. For all BERT-Base models, we chose the training duration to be 286,720,000 samples of sequence length 128; this covers 78.6% of C4. 2. Higher Masking Ratio for the Masked Language Modeling Objective: We used the standard Masked Language Modeling (MLM) pretraining objective. While the original BERT paper also included a Next Sentence Prediction (NSP) task in the pretraining objective, subsequent papers have shown this to be unnecessary [Liu et al. 2019](https://arxiv.org/abs/1907.11692). However, we found that a 30% masking ratio led to slight accuracy improvements in both pretraining MLM and downstream GLUE performance. We therefore included this simple change as part of our MosaicBERT training recipe. Recent studies have also found that this simple change can lead to downstream improvements [Wettig et al. 2022](https://arxiv.org/abs/2202.08005). 3. Bfloat16 Precision: We use [bf16 (bfloat16) mixed precision training](https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus) for all the models, where a matrix multiplication layer uses bf16 for the multiplication and 32-bit IEEE floating point for gradient accumulation. We found this to be more stable than using float16 mixed precision. 4. Vocab Size as a Multiple of 64: We increased the vocab size to be a multiple of 8 as well as 64 (i.e. from 30,522 to 30,528). This small constraint is something of [a magic trick among ML practitioners](https://twitter.com/karpathy/status/1621578354024677377), and leads to a throughput speedup. 5. Hyperparameters: For all models, we use Decoupled AdamW with Beta_1=0.9 and Beta_2=0.98, and a weight decay value of 1.0e-5. The learning rate schedule begins with a warmup to a maximum learning rate of 5.0e-4 followed by a linear decay to zero. Warmup lasted for 6% of the full training duration. Global batch size was set to 4096, and microbatch size was 128; since global batch size was 4096, full pretraining consisted of 70,000 batches. We set the maximum sequence length during pretraining to 128, and we used the standard embedding dimension of 768. For MosaicBERT, we applied 0.1 dropout to the feedforward layers but no dropout to the FlashAttention module, as this was not possible with the OpenAI triton implementation. Full configuration details for pretraining MosaicBERT-Base can be found in the configuration yamls [in the mosaicml/examples repo here](https://github.com/mosaicml/examples/blob/main/examples/benchmarks/bert/yamls/main/mosaic-bert-base-uncased.yaml). ## Evaluation results When fine-tuned on downstream tasks (following the [finetuning details here](https://github.com/mosaicml/examples/tree/main/examples/benchmarks/bert/yamls/finetuning/glue/mosaic-bert-base-uncased.yaml)), the MosaicBERT model achieves the following GLUE results: \| Task \| MNLI-(m/mm) \| QQP \| QNLI \| SST-2 \| CoLA \| STS-B \| MRPC \| RTE \| Average \| \|:----:\|:-----------:\|:----:\|:----:\|:-----:\|:----:\|:-----:\|:----:\|:----:\|:-------:\| \| \| 0.8495 \| 0.9029 \| 0.9074\| 0.9246 \| 0.5511 \| 0.8927 \| 0.9003 \| 0.8136 \| 0.8428 \| Note that this is averaged over n=5 pretraining seeds. ## Collection of MosaicBERT-Base models trained using ALiBi on different sequence lengths ALiBi allows a model trained with a sequence length n to easily extrapolate to sequence lengths >2n during finetuning. For more details, see [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation (Press et al. 2022)](https://arxiv.org/abs/2108.12409) This model is part of the family of MosaicBERT-Base models trained using ALiBi on different sequence lengths: * mosaic-bert-base (trained on a sequence length of 128 tokens) * [mosaic-bert-base-seqlen-256](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-256) * [mosaic-bert-base-seqlen-512](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-512) * [mosaic-bert-base-seqlen-1024](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-1024) * [mosaic-bert-base-seqlen-2048](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) The primary use case of these models is for research on efficient pretraining and finetuning for long context embeddings. ## Intended uses & limitations This model is intended to be finetuned on downstream tasks. ## Citation Please cite this model using the following format: ``` @article{portes2023MosaicBERT, title={MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining}, author={Jacob Portes, Alexander R Trott, Sam Havens, Daniel King, Abhinav Venigalla, Moin Nadeem, Nikhil Sardana, Daya Khudia, Jonathan Frankle}, journal={NeuRIPS https://openreview.net/pdf?id=5zipcfLC2Z}, year={2023}, } ```
Sookeyy/flan-t5-base-code_search_net	Sookeyy	"2024-01-27T18:41:27Z"	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:code_search_net", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-01-27T17:11:39Z"	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer datasets: - code_search_net model-index: - name: flan-t5-base-code_search_net results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base-code_search_net This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the code_search_net dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
pulkitnitk/dog	pulkitnitk	"2023-04-25T16:50:46Z"	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	"2023-04-25T11:38:48Z"	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - pulkitnitk/dog These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
akaigraham/cat-classifier	akaigraham	"2023-02-28T23:40:02Z"	0	0	fastai	[ "fastai", "region:us" ]	null	"2023-02-28T23:27:50Z"	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
iubeda/ppo-Pyramids	iubeda	"2023-02-18T00:04:04Z"	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	"2023-02-18T00:03:58Z"	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: iubeda/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LoneStriker/Nous-Hermes-2-Mixtral-8x7B-DPO-3.0bpw-h6-exl2	LoneStriker	"2024-01-16T11:46:12Z"	5	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "conversational", "en", "base_model:mistralai/Mixtral-8x7B-v0.1", "base_model:finetune:mistralai/Mixtral-8x7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-01-16T11:39:00Z"	--- base_model: mistralai/Mixtral-8x7B-v0.1 tags: - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation model-index: - name: Nous-Hermes-2-Mixtral-8x7B-DPO results: [] license: apache-2.0 language: - en --- # Nous Hermes 2 - Mixtral 8x7B - DPO ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/btRmXWMG7PXatTs-u3G85.jpeg) ## Model description Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1). The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks. This is the SFT + DPO version of Mixtral Hermes 2, we have also released an SFT only version, for people to find which works best for them, which can be found here: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT ## We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO! # Table of Contents 1. [Example Outputs](#example-outputs) 2. [Benchmark Results](#benchmark-results) - GPT4All - AGIEval - BigBench - Comparison to Mixtral-Instruct 3. [Prompt Format](#prompt-format) 4. [Inference Example Code](#inference-code) 5. [Quantized Models](#quantized-models) ## Example Outputs ### Writing Code for Data Visualization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/QJ5RHrOqB5GMP7ZAZ5NTk.png) ### Writing Cyberpunk Psychedelic Poems ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/wuKnMlM2HBGdyUFO7mY_H.png) ### Performing Backtranslation to Create Prompts from Input Text ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/QElwK1UI9PQQT6WosXpo1.png) ## Benchmark Results Nous-Hermes 2 on Mixtral 8x7B is a major improvement across the board on the benchmarks below compared to the base Mixtral model, and is the first model to beat the flagship Mixtral Finetune by MistralAI. ## GPT4All: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5990\|± \|0.0143\| \| \| \|acc_norm\|0.6425\|± \|0.0140\| \|arc_easy \| 0\|acc \|0.8657\|± \|0.0070\| \| \| \|acc_norm\|0.8636\|± \|0.0070\| \|boolq \| 1\|acc \|0.8783\|± \|0.0057\| \|hellaswag \| 0\|acc \|0.6661\|± \|0.0047\| \| \| \|acc_norm\|0.8489\|± \|0.0036\| \|openbookqa \| 0\|acc \|0.3440\|± \|0.0213\| \| \| \|acc_norm\|0.4660\|± \|0.0223\| \|piqa \| 0\|acc \|0.8324\|± \|0.0087\| \| \| \|acc_norm\|0.8379\|± \|0.0086\| \|winogrande \| 0\|acc \|0.7616\|± \|0.0120\| ``` Average: 75.70 ## AGIEval: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2402\|± \|0.0269\| \| \| \|acc_norm\|0.2520\|± \|0.0273\| \|agieval_logiqa_en \| 0\|acc \|0.4117\|± \|0.0193\| \| \| \|acc_norm\|0.4055\|± \|0.0193\| \|agieval_lsat_ar \| 0\|acc \|0.2348\|± \|0.0280\| \| \| \|acc_norm\|0.2087\|± \|0.0269\| \|agieval_lsat_lr \| 0\|acc \|0.5549\|± \|0.0220\| \| \| \|acc_norm\|0.5294\|± \|0.0221\| \|agieval_lsat_rc \| 0\|acc \|0.6617\|± \|0.0289\| \| \| \|acc_norm\|0.6357\|± \|0.0294\| \|agieval_sat_en \| 0\|acc \|0.8010\|± \|0.0279\| \| \| \|acc_norm\|0.7913\|± \|0.0284\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4806\|± \|0.0349\| \| \| \|acc_norm\|0.4612\|± \|0.0348\| \|agieval_sat_math \| 0\|acc \|0.4909\|± \|0.0338\| \| \| \|acc_norm\|0.4000\|± \|0.0331\| ``` Average: 46.05 ## BigBench: ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|-----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.6105\|± \|0.0355\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.7182\|± \|0.0235\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.5736\|± \|0.0308\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.4596\|± \|0.0263\| \| \| \|exact_str_match \|0.0000\|± \|0.0000\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.3500\|± \|0.0214\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2500\|± \|0.0164\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.5200\|± \|0.0289\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.3540\|± \|0.0214\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.5000\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.6900\|± \|0.0103\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.6317\|± \|0.0228\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.2535\|± \|0.0138\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.7293\|± \|0.0331\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.6744\|± \|0.0149\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.7400\|± \|0.0139\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2176\|± \|0.0117\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1543\|± \|0.0086\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.5200\|± \|0.0289\| ``` Average: 49.70 # Benchmark Comparison Charts ## GPT4All ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/HK6bSbMfxX_qzxReAcJH9.png) ## AGI-Eval ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/bs3ZvvEACa5Gm4p1JBsZ4.png) ## BigBench Reasoning Test ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/wcceowcVpI12UxliwkOja.png) ## Comparison to Mixtral Instruct: Our benchmarks show gains in many benchmarks against Mixtral Instruct v0.1, on average, beating the flagship Mixtral model. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/7-JtX01p8c4tcgOU28BRJ.png) # Prompt Format Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Inference Code Here is example code using HuggingFace Transformers to inference the model (note: even in 4bit, it will require more than 24GB of VRAM) ```python # Code to inference Hermes with HF Transformers # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages import torch from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import LlamaTokenizer, MixtralForCausalLM import bitsandbytes, flash_attn tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True) model = MixtralForCausalLM.from_pretrained( "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, load_in_4bit=True, use_flash_attention_2=True ) prompts = [ """<\|im_start\|>system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|>user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<\|im_end\|> <\|im_start\|>assistant""", ] for chat in prompts: print(chat) input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Response: {response}") ``` # Quantized Models: ## All sizes of GGUF Quantizations are available here: ### SFT+DPO Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF ### SFT Only Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
mesa44/Reinforce-PixelCopter	mesa44	"2024-01-16T10:40:45Z"	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	"2024-01-16T10:39:39Z"	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 54.00 +/- 35.54 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Wheatley961/Raw_3_no_1_Test_2_new.model	Wheatley961	"2022-11-16T10:43:52Z"	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	"2022-11-16T10:43:28Z"	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 80 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 80, "warmup_steps": 8, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
J1k/Jennie_LoRA	J1k	"2024-02-26T02:40:28Z"	1	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2024-02-23T09:25:59Z"	--- license: openrail++ library_name: diffusers tags: - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK dog widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - J1k/Jennie_LoRA <Gallery /> ## Model description These are J1k/Jennie_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](J1k/Jennie_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
kromcomp/L3.1-test-r128-LoRA	kromcomp	"2025-02-26T05:12:46Z"	0	0	peft	[ "peft", "safetensors", "mergekit", "base_model:Delta-Vector/Control-Nanuq-8B", "base_model:adapter:Delta-Vector/Control-Nanuq-8B", "region:us" ]	null	"2025-02-26T05:11:33Z"	--- base_model: - arcee-ai/Llama-3.1-SuperNova-Lite - Delta-Vector/Control-Nanuq-8B library_name: peft tags: - mergekit - peft --- # test-r128-LoRA This is a LoRA extracted from a language model. It was extracted using [mergekit](https://github.com/arcee-ai/mergekit). ## LoRA Details This LoRA adapter was extracted from [Delta-Vector/Control-Nanuq-8B](https://huggingface.co/Delta-Vector/Control-Nanuq-8B) and uses [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) as a base. ### Parameters The following command was used to extract this LoRA adapter: ```sh /usr/local/bin/mergekit-extract-lora --out-path=loras/test-r128-LoRA --model=Delta-Vector/Control-Nanuq-8B --base-model=arcee-ai/Llama-3.1-SuperNova-Lite --no-lazy-unpickle --max-rank=128 --sv-epsilon=0 --cuda -v -e lm_head ```
gokuls/bert-base-emotion_24	gokuls	"2023-06-21T08:57:27Z"	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-06-21T08:33:25Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: bert-base-emotion_24 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.8611 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-emotion_24 This model is a fine-tuned version of [gokuls/bert_base_24](https://huggingface.co/gokuls/bert_base_24) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.5612 - Accuracy: 0.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.736 \| 1.0 \| 250 \| 0.4242 \| 0.8565 \| \| 0.3013 \| 2.0 \| 500 \| 0.3314 \| 0.8845 \| \| 0.2014 \| 3.0 \| 750 \| 0.3442 \| 0.8905 \| \| 0.1392 \| 4.0 \| 1000 \| 0.3276 \| 0.8915 \| \| 0.1072 \| 5.0 \| 1250 \| 0.3833 \| 0.89 \| \| 0.0783 \| 6.0 \| 1500 \| 0.4205 \| 0.8895 \| \| 0.0559 \| 7.0 \| 1750 \| 0.5287 \| 0.8865 \| \| 0.0378 \| 8.0 \| 2000 \| 0.5459 \| 0.8865 \| \| 0.027 \| 9.0 \| 2250 \| 0.5612 \| 0.8925 \| \| 0.02 \| 10.0 \| 2500 \| 0.5601 \| 0.8915 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.0 - Tokenizers 0.13.3
DBangshu/V4_Base_GPT2_e5_4_1	DBangshu	"2024-11-29T17:40:34Z"	135	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-11-29T17:40:19Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
beyoru/Herto2-27	beyoru	"2025-02-27T02:56:25Z"	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-27T02:55:32Z"	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mcditoos/PPO-LunarLander-v2	mcditoos	"2022-05-05T07:12:33Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2022-05-05T07:11:57Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 233.04 +/- 17.51 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code
janbakker/o-bartpara-wikiauto	janbakker	"2024-11-05T13:13:15Z"	107	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "doc_simp", "wiki-auto", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-03-12T14:18:43Z"	--- language: - en tags: - doc_simp - wiki-auto --- # O->BART_para document simplification system This is the plan-guided paragraph-level BART model pretrained on wiki-auto. It belongs to the paper [Beyond Sentence-level Text Simplification](https://aclanthology.org/2024.determit-1.3/): Reproducibility Study of Context-Aware Document Simplification. The [doc_simp](https://github.com/JanB100/doc_simp) library should be used to interface with this model.
JFJFJFen/flan-t5-large-lora	JFJFJFen	"2023-12-02T13:04:26Z"	3	0	peft	[ "peft", "arxiv:1910.09700", "base_model:google/flan-t5-base", "base_model:adapter:google/flan-t5-base", "region:us" ]	null	"2023-12-02T13:04:00Z"	--- library_name: peft base_model: google/flan-t5-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2.dev0
iloncka/exp_5_objects-subs_1_v_5_xresnet50_ep_60	iloncka	"2024-06-03T09:02:45Z"	0	0	fastai	[ "fastai", "region:us" ]	null	"2024-06-03T09:01:07Z"	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
shohei1029/dreambooth-shohei-28	shohei1029	"2023-08-22T15:21:00Z"	0	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2023-08-21T18:12:50Z"	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: Full body profile photo of a man shohei in various situation tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - shohei1029/dreambooth-shohei-28 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on Full body profile photo of a man shohei in various situation using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
pwork7/gemma7b_meta_math_3epoch_with_kn	pwork7	"2024-11-10T21:05:12Z"	36	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-11-10T21:00:43Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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PrunaAI/chihoonlee10-T3Q-ko-solar-dpo-v6.0-bnb-4bit-smashed	PrunaAI	"2024-07-22T22:30:14Z"	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "base_model:chihoonlee10/T3Q-ko-solar-dpo-v6.0", "base_model:quantized:chihoonlee10/T3Q-ko-solar-dpo-v6.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-07-22T22:27:26Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: chihoonlee10/T3Q-ko-solar-dpo-v6.0 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo chihoonlee10/T3Q-ko-solar-dpo-v6.0 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/chihoonlee10-T3Q-ko-solar-dpo-v6.0-bnb-4bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("chihoonlee10/T3Q-ko-solar-dpo-v6.0") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model chihoonlee10/T3Q-ko-solar-dpo-v6.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
henryscheible/mrpc_bert-base-uncased_81	henryscheible	"2023-01-18T20:15:43Z"	0	0	null	[ "pytorch", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "region:us" ]	null	"2023-01-18T20:03:00Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: mrpc_bert-base-uncased_81 results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8088235294117647 - name: F1 type: f1 value: 0.8717105263157895 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mrpc_bert-base-uncased_81 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6390 - Accuracy: 0.8088 - F1: 0.8717 - Combined Score: 0.8403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
roshinishetty333/llama-2-7b-prompt-tuned	roshinishetty333	"2024-05-17T11:34:59Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-05-11T18:01:21Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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apta/ppo-LunarLander-v2	apta	"2023-01-24T00:18:44Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-01-24T00:18:08Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.85 +/- 21.70 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
guilxus/b45e5438-c086-4061-8ee4-0d4535782ac8	guilxus	"2025-02-04T04:19:34Z"	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-02-04T03:48:02Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B tags: - axolotl - generated_from_trainer model-index: - name: b45e5438-c086-4061-8ee4-0d4535782ac8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cf971d07e3ff665f_train_data.json ds_type: json format: custom path: /workspace/input_data/cf971d07e3ff665f_train_data.json type: field_input: labels field_instruction: name field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: guilxus/b45e5438-c086-4061-8ee4-0d4535782ac8 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/cf971d07e3ff665f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: fef0eb04-6ba6-4379-a2e7-a7fdc70a6b88 wandb_project: Gradients-On-11 wandb_run: your_name wandb_runid: fef0eb04-6ba6-4379-a2e7-a7fdc70a6b88 warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # b45e5438-c086-4061-8ee4-0d4535782ac8 This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6879 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.8455 \| 0.0113 \| 200 \| 3.6879 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MRNH/q-FrozenLake-v1-4x4-noSlippery	MRNH	"2023-08-10T17:06:53Z"	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-08-10T17:06:50Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="MRNH/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
AlTheMan/LLama3-reviewClassifier-Lora-weights	AlTheMan	"2024-05-14T23:15:44Z"	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-05-07T16:21:23Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This is a Llama 3:8b based fine-tuned model used for for classifying reviews as useful for improvement of an application or not. A review was considered useful if it contained something useful for the further development or improvement of the application, that is, a user story, bug report, or other specific problems that users have. It does not include general complaints such as “I hate this app”, or encouragements such as “I like your payment feature”, even if these encouragements are directed at a specific feature of the app. The idea is to use this model as part of a pipeline, where you classify reviews as 'useful' or not, and then you can further tag the useful reviews according to your need and store them. This allows your organisation to send the right reviews to different departments in your organisation to be handled, for example feature requests and bug reports. This model outperformed Chat-bison for this classification task if you weigh the result higher for recall. It had an F1-weighted score of 0.90 for the 550 reviews it was tested against, with an F1-score of 0.92 for the negative class and F1-score of 0.87 for the positive class. The model is fine-tuned to answers "True" or "False" for inputted reviews. It was trained using unsloth AI, based on Llama-3-8b-bnb-4bit, also using TRL SFTTrainer for 10 epochs with a validation loss of 0.0235. It was trained on real reviews from the Google Play store using the following prompt: """ You are an AI trained to classify user reviews for a company called Kivra, which offers a digital mailbox service featuring invoice payment, digital signing, mail scanning, simple bookkeeping, and digital receipts. Your task is to analyze each user review and determine whether it contains useful information such as feature requests, bug reports, or other valuable feedback that can aid the developers in improving the service. You will classify the review as 'True' if it contains such useful information, and 'False' if it does not. Classification Guidelines: - Classify a review as 'True' if it suggests a new feature, reports a bug, complains in a way that highlights a problem, or provides any other information that could lead to an improvement of the service. - Classify as 'False' if the review only praises the service, comments on its current state without constructive feedback, or is irrelevant to service development. Example Classifications: - Review: "It's difficult to increase text size" Classification: True Reason: The review suggests a feature to increase text size, which is useful feedback for enhancing readability. - Review: "This is the best app to pay bills" Classification: False Reason: The review praises the service but lacks suggestions for improvement or reports of issues. - Review: "The app crashes every time I try to upload a document." Classification: True Reason: The review reports a specific bug, providing critical information for troubleshooting and improvement.""" ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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fhai50032/llama-8b-hindi-hinglish-cot	fhai50032	"2025-03-02T22:18:43Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-02T22:04:13Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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StoriesLM/StoriesLM-v1-1916	StoriesLM	"2024-03-09T23:09:13Z"	101	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "en", "dataset:dell-research-harvard/AmericanStories", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2024-03-09T22:23:33Z"	--- license: mit datasets: - dell-research-harvard/AmericanStories language: - en --- # StoriesLM: A Family of Language Models With Sequentially-Expanding Pretraining Windows ## Model Family StoriesLM is a family of language models with sequentially-expanding pretraining windows. The pretraining data for the model family comes from the American Stories dataset—a collection of language from historical American news articles. The first language model in the StoriesLM family is pretrained on language data from 1900. Each subsequent language model further trains the previous year’s model checkpoint using data from the following year, up until 1963. ## Dataset The StoriesLM family is pretrained on the American Stories dataset. If you use a model from this family, please also cite the original dataset's authors: ``` @article{dell2024american, title={American stories: A large-scale structured text dataset of historical us newspapers}, author={Dell, Melissa and Carlson, Jacob and Bryan, Tom and Silcock, Emily and Arora, Abhishek and Shen, Zejiang and D'Amico-Wong, Luca and Le, Quan and Querubin, Pablo and Heldring, Leander}, journal={Advances in Neural Information Processing Systems}, volume={36}, year={2024} } ```
ericrisco/llme2_sft_model_rlaif	ericrisco	"2025-01-13T20:24:52Z"	66	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-03-21T05:46:29Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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nat-hunt/14b0cb2e-dafa-4206-b4ab-2f4022dbc825	nat-hunt	"2025-01-13T18:37:45Z"	14	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/llama-2-7b-chat", "base_model:adapter:unsloth/llama-2-7b-chat", "license:apache-2.0", "region:us" ]	null	"2025-01-13T18:34:52Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/llama-2-7b-chat tags: - axolotl - generated_from_trainer model-index: - name: 14b0cb2e-dafa-4206-b4ab-2f4022dbc825 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/llama-2-7b-chat bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fe865ab9c1c0ecd8_train_data.json ds_type: json format: custom path: /workspace/input_data/fe865ab9c1c0ecd8_train_data.json type: field_input: statement_supporting_documents field_instruction: statement field_output: response_positive format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: nat-hunt/14b0cb2e-dafa-4206-b4ab-2f4022dbc825 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/fe865ab9c1c0ecd8_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4af517d6-9e46-423a-b226-51765e2b2f71 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4af517d6-9e46-423a-b226-51765e2b2f71 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 14b0cb2e-dafa-4206-b4ab-2f4022dbc825 This model is a fine-tuned version of [unsloth/llama-2-7b-chat](https://huggingface.co/unsloth/llama-2-7b-chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0014 \| 1 \| nan \| \| 0.0 \| 0.0042 \| 3 \| nan \| \| 0.6006 \| 0.0084 \| 6 \| nan \| \| 0.0 \| 0.0126 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
HQ-Izzy-Publilsh-New-Video/Izzy.New.Video.Tutorial.Official.Viral.On.Instagram	HQ-Izzy-Publilsh-New-Video	"2025-03-02T08:23:11Z"	0	0	null	[ "region:us" ]	null	"2025-03-02T08:23:01Z"	<a href="http://bit.ly/3ZBGcrZ"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="http://bit.ly/3ZBGcrZ" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="http://bit.ly/3ZBGcrZ" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
helamri/q-FrozenLake-v1-4x4-noSlippery	helamri	"2023-08-05T12:23:35Z"	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-08-05T12:23:31Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="helamri/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
FIVE-MGI/Single_Neuron_Identification	FIVE-MGI	"2024-06-13T18:18:09Z"	0	0	tf-keras	[ "tf-keras", "onnx", "en", "dataset:FIVE-MGI/SNIM20", "license:agpl-3.0", "region:us" ]	null	"2024-06-13T17:48:37Z"	--- license: agpl-3.0 language: - en datasets: - FIVE-MGI/SNIM20 --- # Single Neuron (or Cell) Identification This model is designed for image classification and identifying rafts or regions that have single neurons and are trained on [FIVE-MGI/SNIM20]. ## Research Paper For more detailed information, please refer to our bioRxiv paper: [Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement](https://www.biorxiv.org/content/10.1101/2023.12.24.573194)
cjpais/llava-1.6-mistral-7b-gguf	cjpais	"2024-03-06T20:16:02Z"	18,498	98	null	[ "gguf", "llava", "image-text-to-text", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	"2024-02-01T20:44:59Z"	--- license: apache-2.0 tags: - llava pipeline_tag: image-text-to-text --- # GGUF Quantized LLaVA 1.6 Mistral 7B Updated quants and projector from [PR #5267](https://github.com/ggerganov/llama.cpp/pull/5267) ## Provided files \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [llava-v1.6-mistral-7b.Q3_K_XS.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q3_K_XS.gguf) \| Q3_K_XS \| 3 \| 2.99 GB\| very small, high quality loss \| \| [llava-v1.6-mistral-7b.Q3_K_M.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| very small, high quality loss \| \| [llava-v1.6-mistral-7b.Q4_K_M.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| medium, balanced quality - recommended \| \| [llava-v1.6-mistral-7b.Q5_K_S.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| large, low quality loss - recommended \| \| [llava-v1.6-mistral-7b.Q5_K_M.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| large, very low quality loss - recommended \| \| [llava-v1.6-mistral-7b.Q6_K.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| very large, extremely low quality loss \| \| [llava-v1.6-mistral-7b.Q8_0.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/llava-v1.6-mistral-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.7 GB\| very large, extremely low quality loss - not recommended \| <br> <br> # ORIGINAL LLaVA Model Card ## Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) Model date: LLaVA-v1.6-Mistral-7B was trained in December 2023. Paper or resources for more information: https://llava-vl.github.io/ ## License [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) license. Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues ## Intended use Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 50K GPT-4V data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
benito14/SOIT_Llama3.2	benito14	"2024-11-12T09:54:27Z"	5	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-1B-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-1B-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-11-12T09:54:07Z"	--- base_model: unsloth/Llama-3.2-1B-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf --- # Uploaded model - Developed by: benito14 - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Free_Sydney_13b_HF-i1-GGUF	mradermacher	"2024-08-02T10:43:30Z"	255	0	transformers	[ "transformers", "gguf", "LLaMA", "LLM", "Sydney", "en", "base_model:FPHam/Free_Sydney_13b_HF", "base_model:quantized:FPHam/Free_Sydney_13b_HF", "endpoints_compatible", "region:us", "imatrix" ]	null	"2024-06-11T15:58:39Z"	--- base_model: FPHam/Free_Sydney_13b_HF language: - en library_name: transformers quantized_by: mradermacher tags: - LLaMA - LLM - Sydney --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/FPHam/Free_Sydney_13b_HF <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Free_Sydney_13b_HF-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q2_K.gguf) \| i1-Q2_K \| 5.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ3_M.gguf) \| i1-IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 7.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Free_Sydney_13b_HF-i1-GGUF/resolve/main/Free_Sydney_13b_HF.i1-Q6_K.gguf) \| i1-Q6_K \| 10.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ayushgs/mistral-7b-v0.2-instruct-mh-1500	ayushgs	"2024-04-18T05:03:25Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-04-18T05:02:36Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - Developed by: ayushgs - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LoneStriker/Smaug-72B-v0.1-6.0bpw-h6-exl2	LoneStriker	"2024-02-17T20:00:11Z"	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "base_model:moreh/MoMo-72B-lora-1.8.7-DPO", "base_model:finetune:moreh/MoMo-72B-lora-1.8.7-DPO", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-02-17T19:28:30Z"	--- license: other license_name: tongyi-qianwen-license-agreement license_link: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT base_model: moreh/MoMo-72B-lora-1.8.7-DPO --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/e4u8VYfDBh11u60rFYJHF.png) Smaug arrives! We recently released Smaug-72B-v0.1 which has taken first place on the Open LLM Leaderboard by HuggingFace. It is the first open-source model to have an average score more than 80. Smaug-72B is finetuned directly from [moreh/MoMo-72B-lora-1.8.7-DPO](https://huggingface.co/moreh/MoMo-72B-lora-1.8.7-DPO) and is ultimately based on [Qwen-72B](https://huggingface.co/Qwen/Qwen-72B). To do so we built on techniques and datasets used in our previous model efforts, with the addition of some new datasets and a new approach. We believe this new approach is generally useful in training across a wide range of model types and downstream use cases, and it powers both our Smaug-34B and 72B models. We are currently working on writing up this new technique in the form of a technical report which we aim to release on arXiv soon (we may also be releasing a new member of the Smaug lineup at that time!). We are excited to release the details to the open source community for them to build on and improve Smaug and spawn more dragons to dominate the LLM space. Keep watching this space for our announcements! ### Evaluation Results \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Winogrande \| GSM8K \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 80.48 \| 76.02 \| 89.27 \| 77.15 \| 76.67 \| 85.08 \| 78.70 \| #### MT-Bench We ran MT-Bench with the llama-2 conversation template and the system prompt set to the Qwen system prompt. We got the following results in single model mode: \| First Turn \| Second Turn \| Average \| \| ---------- \| ----------- \| ------- \| \| 8.18 \| 7.34 \| 7.76 \| The bottom of the card includes some sample responses from the model. We have selected responses that have high and low scores from the GPT-4 judge. ### Contamination Results We generate our contamination numbers using https://github.com/swj0419/detect-pretrain-code-contamination/tree/master, with Llama7B as our reference model. Smaug-72B has the following results: \| ARC \| TruthfulQA \| GSM8K \| \| --- \| --- \| --- \| \| 0.20\| 0.45\| 1.00\| By comparison, MoMo-72B-lora-1.8.7-DPO has the following results: \| ARC \| TruthfulQA \| GSM8K \| \| --- \| --- \| --- \| \| 0.20\| 0.39\| 1.00\| Note that GSM8K often scores very highly on this contamination suite - we verified this by also running Llama-2-70B: \| ARC \| TruthfulQA \| GSM8K \| \| --- \| --- \| --- \| \| 0.22\| 0.51\| 0.89\| ### Sample MT Bench results #### Example 1 Question Imagine you are writing a blog post comparing two popular smartphone models. Develop an outline for the blog post, including key points and subheadings to effectively compare and contrast the features, performance, and user experience of the two models. Please answer in fewer than 200 words. Response Outline: 1. Introduction to the two smartphone models being compared (mention brand names and models) 2. Design and Build Quality - Comparison of dimensions, weight, materials used, and overall aesthetic appeal - Water and dust resistance, if applicable 3. Display and Screen Quality - Comparison of screen technology (OLED, IPS LCD, etc.) - Display resolution, refresh rate, and brightness levels 4. Performance and Hardware - Processor, RAM, and storage configurations - Comparison of benchmark scores (e.g., AnTuTu, Geekbench) 5. Camera System and Image Quality - Comparison of primary, ultra-wide, telephoto, and macro lenses (if available) - Comparison of low-light performance and image processing capabilities 6. Battery Life and Charging - Battery capacity and estimated usage time - Charging technologies (wired, wireless, fast charging, reverse charging) 7. Software and User Experience - Comparison of operating systems (iOS vs. Android) and their features - User interface, customization options, and app ecosystem 8. Additional Features and Extras - Comparison of biometric authentication (fingerprint, face unlock, etc.) - Additional features like NFC, 5G connectivity, and audio quality 9. Pricing and Value for Money - Comparison of retail prices and current offers - Assessment of overall value considering the features and performance 10. Conclusion and recommendation for potential buyers based on their preferences #### Example 2 Question In a survey conducted at a local high school, preferences for a new school color were measured: 58% of students liked the color blue, 45% preferred green, and 22% liked both colors. If we randomly pick a student from the school, what's the probability that they would like neither blue nor green? Answer To find the probability that a randomly picked student would like neither blue nor green, we need to find the percentage of students who like only one color or neither color. From the given information, we know: - 58% like blue - 45% prefer green - 22% like both colors To find the percentage of students who like only one color, we can subtract the percentage of students who like both colors from the sum of students who like blue and green: - 58% (blue) + 45% (green) - 22% (both) = 83% Now, to find the probability of a student liking neither blue nor green, we subtract the percentage of students who like at least one color from 100%: - 100% - 83% = 17% So, the probability that a randomly picked student would like neither blue nor green is 17%. #### Example 3 Question Implement a program to find the common elements in two arrays without using any extra data structures. Answer Finding common elements in two arrays without using extra data structures can be achieved by using a simple algorithm. Here's a Python implementation of this problem: ```python def find_common_elements(arr1, arr2): common_elements = [] for num1 in arr1: if num1 in arr2 and num1 not in common_elements: common_elements.append(num1) return common_elements # Example usage: array1 = [1, 2, 3, 4, 5] array2 = [4, 5, 6, 7, 8] print(find_common_elements(array1, array2)) # Output: [4, 5] ``` This program iterates through the elements of the first array (arr1) and checks if each element is present in the second array (arr2). If it is, and the element hasn't been added to the common_elements list yet, it is appended. The function then returns the list of common elements. #### Example 4 Question Identify the named entities (people, organizations, locations) mentioned in the given news article. Please generate a JSON dictionary that lists the named entities in three separate groups based on their entity types. The key is the type of entity and the value is a list of strings. Yesterday, Adamson Emerson, the CEO of Faraday, and Dieter Zetsche, the CEO of Daimler AG, announced plans to build a new Gigafactory in Berlin. The facility will be a joint venture between Faraday and Daimler, producing electric vehicles and battery packs for both companies, creating thousands of job opportunities in the region. Emerson and Zetsche stated that the strategic location of Berlin, coupled with its skilled workforce and strong infrastructure, makes it an ideal choice for expansion. The new Gigafactory aims to meet the growing demand for electric vehicles in Europe and contribute to a sustainable future. Volkswagen CEO Herbert Diess welcomed the news, saying greater collaboration will, benefit the auto industry's transition to e-mobility. Answer ``` { "people": ["Adamson Emerson", "Dieter Zetsche", "Herbert Diess"], "organizations": ["Faraday", "Daimler AG", "Volkswagen"], "locations": ["Berlin", "Europe"] } ```
ayushtues/blipdiffusion	ayushtues	"2023-09-21T14:44:10Z"	7	0	diffusers	[ "diffusers", "safetensors", "en", "arxiv:2305.14720", "license:apache-2.0", "diffusers:BlipDiffusionPipeline", "region:us" ]	null	"2023-08-07T05:45:01Z"	--- license: apache-2.0 language: - en library_name: diffusers --- # BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing <!-- Provide a quick summary of what the model is/does. --> Model card for BLIP-Diffusion, a text to image Diffusion model which enables zero-shot subject-driven generation and control-guided zero-shot generation. The abstract from the paper is: Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. The model is created by Dongxu Li, Junnan Li, Steven C.H. Hoi. ### Model Sources <!-- Provide the basic links for the model. --> - Original Repository: https://github.com/salesforce/LAVIS/tree/main - Project Page: https://dxli94.github.io/BLIP-Diffusion-website/ ## Uses ### Zero-Shot Subject Driven Generation ```python from diffusers.pipelines import BlipDiffusionPipeline from diffusers.utils import load_image import torch blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained( "Salesforce/blipdiffusion", torch_dtype=torch.float16 ).to("cuda") cond_subject = "dog" tgt_subject = "dog" text_prompt_input = "swimming underwater" cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" ) iter_seed = 88888 guidance_scale = 7.5 num_inference_steps = 25 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt_input, cond_image, cond_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg" style="width:500px;"/> Generatred Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog_underwater.png" style="width:500px;"/> ### Controlled subject-driven generation ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import CannyDetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16 ).to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "teapot" # subject to generate. text_prompt = "on a marble table" cldm_cond_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" ).resize((512, 512)) canny = CannyDetector() cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type="pil") style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Canny Edge Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/canny_generated.png" style="width:500px;"/> ### Controlled subject-driven generation Scribble ```python from diffusers.pipelines import BlipDiffusionControlNetPipeline from diffusers.utils import load_image from controlnet_aux import HEDdetector blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained( "Salesforce/blipdiffusion-controlnet" ) controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") blip_diffusion_pipe.controlnet = controlnet blip_diffusion_pipe.to("cuda") style_subject = "flower" # subject that defines the style tgt_subject = "bag" # subject to generate. text_prompt = "on a table" cldm_cond_image = load_image( "https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png" ).resize((512, 512)) hed = HEDdetector.from_pretrained("lllyasviel/Annotators") cldm_cond_image = hed(cldm_cond_image) style_image = load_image( "https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" ) guidance_scale = 7.5 num_inference_steps = 50 negative_prompt = "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate" output = blip_diffusion_pipe( text_prompt, style_image, cldm_cond_image, style_subject, tgt_subject, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, neg_prompt=negative_prompt, height=512, width=512, ).images output[0].save("image.png") ``` Input Style Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg" style="width:500px;"/> Scribble Input : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble.png" style="width:500px;"/> Generated Image : <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/scribble_output.png" style="width:500px;"/> ## Model Architecture Blip-Diffusion learns a pre-trained subject representation. uch representation aligns with text embeddings and in the meantime also encodes the subject appearance. This allows efficient fine-tuning of the model for high-fidelity subject-driven applications, such as text-to-image generation, editing and style transfer. To this end, they design a two-stage pre-training strategy to learn generic subject representation. In the first pre-training stage, they perform multimodal representation learning, which enforces BLIP-2 to produce text-aligned visual features based on the input image. In the second pre-training stage, they design a subject representation learning task, called prompted context generation, where the diffusion model learns to generate novel subject renditions based on the input visual features. To achieve this, they curate pairs of input-target images with the same subject appearing in different contexts. Specifically, they synthesize input images by composing the subject with a random background. During pre-training, they feed the synthetic input image and the subject class label through BLIP-2 to obtain the multimodal embeddings as subject representation. The subject representation is then combined with a text prompt to guide the generation of the target image. ![img](https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch.jpg) The architecture is also compatible to integrate with established techniques built on top of the diffusion model, such as ControlNet. They attach the U-Net of the pre-trained ControlNet to that of BLIP-Diffusion via residuals. In this way, the model takes into account the input structure condition, such as edge maps and depth maps, in addition to the subject cues. Since the model inherits the architecture of the original latent diffusion model, they observe satisfying generations using off-the-shelf integration with pre-trained ControlNet without further training. <img src="https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/arch_controlnet.png" style="width:50%;"/> ## Citation BibTeX: If you find this repository useful in your research, please cite: ``` @misc{li2023blipdiffusion, title={BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing}, author={Dongxu Li and Junnan Li and Steven C. H. Hoi}, year={2023}, eprint={2305.14720}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
Ahmed-ibn-Harun/BrainHermorrhage-vit-base	Ahmed-ibn-Harun	"2025-01-07T14:41:28Z"	199	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2025-01-04T13:34:23Z"	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base results: - task: name: Image Classification type: image-classification dataset: name: Ahmed-ibn-Harun/BrainHermorrhage type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8261405672009864 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BrainHermorrhage-vit-base This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Ahmed-ibn-Harun/BrainHermorrhage dataset. It achieves the following results on the evaluation set: - Loss: 0.3755 - Accuracy: 0.8261 - Sensitivity: 0.7221 - Specificity: 0.9289 - F1 Score: 0.8050 - Auc: 0.9162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Sensitivity \| Specificity \| F1 Score \| Auc \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\|:-----------:\|:-----------:\|:--------:\|:------:\| \| 0.3331 \| 0.2188 \| 100 \| 0.4220 \| 0.7891 \| 0.6868 \| 0.8902 \| 0.7641 \| 0.8947 \| \| 0.4904 \| 0.4376 \| 200 \| 0.4409 \| 0.8038 \| 0.7750 \| 0.8324 \| 0.7971 \| 0.8931 \| \| 0.4875 \| 0.6565 \| 300 \| 0.5088 \| 0.8162 \| 0.9009 \| 0.7324 \| 0.8298 \| 0.9057 \| \| 0.4366 \| 0.8753 \| 400 \| 0.3726 \| 0.8314 \| 0.7671 \| 0.8951 \| 0.8190 \| 0.9190 \| \| 0.4663 \| 1.0941 \| 500 \| 0.4225 \| 0.8157 \| 0.8910 \| 0.7412 \| 0.8278 \| 0.9201 \| \| 0.2961 \| 1.3129 \| 600 \| 0.3632 \| 0.8339 \| 0.7929 \| 0.8745 \| 0.8260 \| 0.9244 \| \| 0.3367 \| 1.5317 \| 700 \| 0.4454 \| 0.8117 \| 0.8870 \| 0.7373 \| 0.8241 \| 0.9083 \| \| 0.4084 \| 1.7505 \| 800 \| 0.5469 \| 0.7408 \| 0.9732 \| 0.5108 \| 0.7888 \| 0.9068 \| \| 0.3161 \| 1.9694 \| 900 \| 0.3893 \| 0.8329 \| 0.8672 \| 0.7990 \| 0.8377 \| 0.9282 \| \| 0.4762 \| 2.1882 \| 1000 \| 0.4871 \| 0.7363 \| 0.9633 \| 0.5118 \| 0.7842 \| 0.8974 \| \| 0.4006 \| 2.4070 \| 1100 \| 0.4228 \| 0.7900 \| 0.9326 \| 0.6490 \| 0.8154 \| 0.9257 \| \| 0.4541 \| 2.6258 \| 1200 \| 0.3389 \| 0.8487 \| 0.7641 \| 0.9324 \| 0.8340 \| 0.9349 \| \| 0.5397 \| 2.8446 \| 1300 \| 0.4587 \| 0.7565 \| 0.9732 \| 0.5422 \| 0.7990 \| 0.9244 \| \| 0.2115 \| 3.0635 \| 1400 \| 0.3976 \| 0.8344 \| 0.8196 \| 0.8490 \| 0.8312 \| 0.9223 \| \| 0.3588 \| 3.2823 \| 1500 \| 0.3928 \| 0.8211 \| 0.8949 \| 0.7480 \| 0.8326 \| 0.9208 \| \| 0.3377 \| 3.5011 \| 1600 \| 0.3943 \| 0.8157 \| 0.7483 \| 0.8824 \| 0.8015 \| 0.9128 \| \| 0.3385 \| 3.7199 \| 1700 \| 0.3627 \| 0.8428 \| 0.8256 \| 0.8598 \| 0.8393 \| 0.9247 \| \| 0.3793 \| 3.9387 \| 1800 \| 0.4015 \| 0.8063 \| 0.7592 \| 0.8529 \| 0.7958 \| 0.9007 \| \| 0.2774 \| 4.1575 \| 1900 \| 0.4174 \| 0.8186 \| 0.8018 \| 0.8353 \| 0.8147 \| 0.9090 \| \| 0.2964 \| 4.3764 \| 2000 \| 0.4120 \| 0.8245 \| 0.8940 \| 0.7559 \| 0.8352 \| 0.9243 \| \| 0.2042 \| 4.5952 \| 2100 \| 0.3984 \| 0.8383 \| 0.8414 \| 0.8353 \| 0.8381 \| 0.9214 \| \| 0.2336 \| 4.8140 \| 2200 \| 0.4263 \| 0.8241 \| 0.8722 \| 0.7765 \| 0.8314 \| 0.9242 \| \| 0.2292 \| 5.0328 \| 2300 \| 0.4430 \| 0.8339 \| 0.8186 \| 0.8490 \| 0.8306 \| 0.9241 \| \| 0.265 \| 5.2516 \| 2400 \| 0.4647 \| 0.8314 \| 0.7681 \| 0.8941 \| 0.8192 \| 0.9204 \| \| 0.2754 \| 5.4705 \| 2500 \| 0.5476 \| 0.7886 \| 0.9128 \| 0.6657 \| 0.8111 \| 0.9116 \| \| 0.1859 \| 5.6893 \| 2600 \| 0.4330 \| 0.8324 \| 0.8414 \| 0.8235 \| 0.8332 \| 0.9218 \| \| 0.1785 \| 5.9081 \| 2700 \| 0.4180 \| 0.8369 \| 0.8375 \| 0.8363 \| 0.8362 \| 0.9199 \| \| 0.2057 \| 6.1269 \| 2800 \| 0.4660 \| 0.8319 \| 0.8543 \| 0.8098 \| 0.8349 \| 0.9158 \| \| 0.289 \| 6.3457 \| 2900 \| 0.4399 \| 0.8186 \| 0.8196 \| 0.8176 \| 0.8180 \| 0.9086 \| \| 0.1172 \| 6.5646 \| 3000 \| 0.5597 \| 0.8260 \| 0.8474 \| 0.8049 \| 0.8289 \| 0.9085 \| \| 0.1758 \| 6.7834 \| 3100 \| 0.4902 \| 0.8201 \| 0.8335 \| 0.8069 \| 0.8217 \| 0.9078 \| \| 0.2185 \| 7.0022 \| 3200 \| 0.4738 \| 0.8211 \| 0.8295 \| 0.8127 \| 0.8218 \| 0.9108 \| \| 0.2255 \| 7.2210 \| 3300 \| 0.5072 \| 0.8098 \| 0.8771 \| 0.7431 \| 0.8210 \| 0.9082 \| \| 0.213 \| 7.4398 \| 3400 \| 0.4879 \| 0.8379 \| 0.7919 \| 0.8833 \| 0.8293 \| 0.9126 \| \| 0.1528 \| 7.6586 \| 3500 \| 0.6054 \| 0.8137 \| 0.8276 \| 0.8 \| 0.8154 \| 0.9057 \| \| 0.1834 \| 7.8775 \| 3600 \| 0.5653 \| 0.8260 \| 0.7532 \| 0.8980 \| 0.8115 \| 0.9141 \| \| 0.0812 \| 8.0963 \| 3700 \| 0.6640 \| 0.8176 \| 0.7284 \| 0.9059 \| 0.7989 \| 0.9068 \| \| 0.1422 \| 8.3151 \| 3800 \| 0.5916 \| 0.8255 \| 0.7721 \| 0.8784 \| 0.8149 \| 0.9075 \| \| 0.1116 \| 8.5339 \| 3900 \| 0.5746 \| 0.8226 \| 0.8583 \| 0.7873 \| 0.8279 \| 0.9057 \| \| 0.1811 \| 8.7527 \| 4000 \| 0.4679 \| 0.8359 \| 0.7869 \| 0.8843 \| 0.8267 \| 0.9245 \| \| 0.1949 \| 8.9716 \| 4100 \| 0.4645 \| 0.8201 \| 0.7641 \| 0.8755 \| 0.8086 \| 0.9048 \| \| 0.036 \| 9.1904 \| 4200 \| 0.6486 \| 0.8349 \| 0.7889 \| 0.8804 \| 0.8262 \| 0.9116 \| \| 0.1117 \| 9.4092 \| 4300 \| 0.5656 \| 0.8236 \| 0.7433 \| 0.9029 \| 0.8073 \| 0.9125 \| \| 0.1101 \| 9.6280 \| 4400 \| 0.5197 \| 0.8285 \| 0.8731 \| 0.7843 \| 0.8351 \| 0.9226 \| \| 0.2064 \| 9.8468 \| 4500 \| 0.6169 \| 0.8270 \| 0.7641 \| 0.8892 \| 0.8146 \| 0.9132 \| \| 0.0647 \| 10.0656 \| 4600 \| 0.5593 \| 0.8255 \| 0.7542 \| 0.8961 \| 0.8113 \| 0.9122 \| \| 0.0566 \| 10.2845 \| 4700 \| 0.5822 \| 0.8245 \| 0.7384 \| 0.9098 \| 0.8072 \| 0.9182 \| \| 0.1324 \| 10.5033 \| 4800 \| 0.5593 \| 0.8319 \| 0.7948 \| 0.8686 \| 0.8247 \| 0.9146 \| \| 0.0824 \| 10.7221 \| 4900 \| 0.6553 \| 0.8117 \| 0.7037 \| 0.9186 \| 0.7880 \| 0.9130 \| \| 0.2134 \| 10.9409 \| 5000 \| 0.5847 \| 0.8334 \| 0.8880 \| 0.7794 \| 0.8413 \| 0.9271 \| \| 0.0835 \| 11.1597 \| 5100 \| 0.6585 \| 0.8314 \| 0.8008 \| 0.8618 \| 0.8253 \| 0.9130 \| \| 0.0936 \| 11.3786 \| 5200 \| 0.8768 \| 0.8191 \| 0.7939 \| 0.8441 \| 0.8136 \| 0.9062 \| \| 0.0325 \| 11.5974 \| 5300 \| 0.6502 \| 0.8423 \| 0.8315 \| 0.8529 \| 0.8398 \| 0.9209 \| \| 0.1054 \| 11.8162 \| 5400 \| 0.5742 \| 0.8354 \| 0.8256 \| 0.8451 \| 0.833 \| 0.9186 \| \| 0.0157 \| 12.0350 \| 5500 \| 0.7790 \| 0.8324 \| 0.8256 \| 0.8392 \| 0.8305 \| 0.9150 \| \| 0.0929 \| 12.2538 \| 5600 \| 0.5779 \| 0.8433 \| 0.7978 \| 0.8882 \| 0.8351 \| 0.9183 \| \| 0.0553 \| 12.4726 \| 5700 \| 0.6642 \| 0.8369 \| 0.8157 \| 0.8578 \| 0.8326 \| 0.9154 \| \| 0.1012 \| 12.6915 \| 5800 \| 0.6882 \| 0.8344 \| 0.8494 \| 0.8196 \| 0.8361 \| 0.9218 \| \| 0.1292 \| 12.9103 \| 5900 \| 0.6949 \| 0.8310 \| 0.7800 \| 0.8814 \| 0.8211 \| 0.9114 \| \| 0.103 \| 13.1291 \| 6000 \| 0.7031 \| 0.8398 \| 0.8216 \| 0.8578 \| 0.8361 \| 0.9162 \| \| 0.0652 \| 13.3479 \| 6100 \| 0.7927 \| 0.8379 \| 0.8176 \| 0.8578 \| 0.8338 \| 0.9178 \| \| 0.1194 \| 13.5667 \| 6200 \| 0.7817 \| 0.8211 \| 0.7602 \| 0.8814 \| 0.8086 \| 0.9125 \| \| 0.2684 \| 13.7856 \| 6300 \| 0.7446 \| 0.8221 \| 0.7017 \| 0.9412 \| 0.7968 \| 0.9164 \| \| 0.1194 \| 14.0044 \| 6400 \| 0.7420 \| 0.8334 \| 0.8305 \| 0.8363 \| 0.8322 \| 0.9152 \| \| 0.0548 \| 14.2232 \| 6500 \| 0.8545 \| 0.8295 \| 0.8543 \| 0.8049 \| 0.8329 \| 0.8918 \| \| 0.0681 \| 14.4420 \| 6600 \| 0.8680 \| 0.7915 \| 0.6373 \| 0.9441 \| 0.7525 \| 0.9094 \| \| 0.0627 \| 14.6608 \| 6700 \| 0.6181 \| 0.8487 \| 0.8028 \| 0.8941 \| 0.8407 \| 0.9206 \| \| 0.0565 \| 14.8796 \| 6800 \| 0.7248 \| 0.8241 \| 0.8117 \| 0.8363 \| 0.8211 \| 0.9108 \| \| 0.0879 \| 15.0985 \| 6900 \| 0.6817 \| 0.8295 \| 0.8484 \| 0.8108 \| 0.8319 \| 0.9208 \| \| 0.1235 \| 15.3173 \| 7000 \| 0.7360 \| 0.8344 \| 0.8434 \| 0.8255 \| 0.8351 \| 0.9143 \| \| 0.1256 \| 15.5361 \| 7100 \| 0.6166 \| 0.8300 \| 0.7839 \| 0.8755 \| 0.8210 \| 0.9114 \| \| 0.0353 \| 15.7549 \| 7200 \| 0.7718 \| 0.8339 \| 0.8771 \| 0.7912 \| 0.8401 \| 0.9231 \| \| 0.0838 \| 15.9737 \| 7300 \| 0.7930 \| 0.8305 \| 0.7919 \| 0.8686 \| 0.8229 \| 0.8963 \| \| 0.0345 \| 16.1926 \| 7400 \| 0.9201 \| 0.8231 \| 0.7304 \| 0.9147 \| 0.8041 \| 0.8816 \| \| 0.0263 \| 16.4114 \| 7500 \| 0.8642 \| 0.8310 \| 0.7433 \| 0.9176 \| 0.8139 \| 0.9021 \| \| 0.0471 \| 16.6302 \| 7600 \| 0.8542 \| 0.8324 \| 0.7631 \| 0.9010 \| 0.8191 \| 0.9031 \| \| 0.0894 \| 16.8490 \| 7700 \| 0.7756 \| 0.8034 \| 0.7116 \| 0.8941 \| 0.7826 \| 0.8999 \| \| 0.0649 \| 17.0678 \| 7800 \| 0.7112 \| 0.8344 \| 0.8484 \| 0.8206 \| 0.8359 \| 0.9077 \| \| 0.0567 \| 17.2867 \| 7900 \| 0.7433 \| 0.8452 \| 0.8394 \| 0.8510 \| 0.8436 \| 0.9106 \| \| 0.0229 \| 17.5055 \| 8000 \| 0.8775 \| 0.8255 \| 0.7542 \| 0.8961 \| 0.8113 \| 0.9017 \| \| 0.009 \| 17.7243 \| 8100 \| 0.8561 \| 0.8349 \| 0.7958 \| 0.8735 \| 0.8274 \| 0.9062 \| \| 0.0838 \| 17.9431 \| 8200 \| 0.9441 \| 0.8255 \| 0.8771 \| 0.7745 \| 0.8333 \| 0.9211 \| \| 0.0958 \| 18.1619 \| 8300 \| 0.9286 \| 0.8255 \| 0.7374 \| 0.9127 \| 0.8078 \| 0.8961 \| \| 0.0422 \| 18.3807 \| 8400 \| 0.8053 \| 0.8369 \| 0.8186 \| 0.8549 \| 0.8331 \| 0.9114 \| \| 0.053 \| 18.5996 \| 8500 \| 0.8440 \| 0.8388 \| 0.8067 \| 0.8706 \| 0.8327 \| 0.8972 \| \| 0.0462 \| 18.8184 \| 8600 \| 0.7419 \| 0.8221 \| 0.8137 \| 0.8304 \| 0.8198 \| 0.9108 \| \| 0.0474 \| 19.0372 \| 8700 \| 0.8702 \| 0.8231 \| 0.7849 \| 0.8608 \| 0.8152 \| 0.8997 \| \| 0.0257 \| 19.2560 \| 8800 \| 0.8966 \| 0.8157 \| 0.7473 \| 0.8833 \| 0.8013 \| 0.9049 \| \| 0.0214 \| 19.4748 \| 8900 \| 0.9787 \| 0.8275 \| 0.7839 \| 0.8706 \| 0.8188 \| 0.8877 \| \| 0.1409 \| 19.6937 \| 9000 \| 0.8695 \| 0.8379 \| 0.7899 \| 0.8853 \| 0.8289 \| 0.9084 \| \| 0.0715 \| 19.9125 \| 9100 \| 0.9500 \| 0.8245 \| 0.8028 \| 0.8461 \| 0.8198 \| 0.8975 \| \| 0.0331 \| 20.1313 \| 9200 \| 0.9371 \| 0.8334 \| 0.8375 \| 0.8294 \| 0.8333 \| 0.9042 \| \| 0.0259 \| 20.3501 \| 9300 \| 0.8587 \| 0.8374 \| 0.8127 \| 0.8618 \| 0.8325 \| 0.9124 \| \| 0.0093 \| 20.5689 \| 9400 \| 0.7861 \| 0.8393 \| 0.8196 \| 0.8588 \| 0.8354 \| 0.9182 \| \| 0.0103 \| 20.7877 \| 9500 \| 0.7921 \| 0.8359 \| 0.7800 \| 0.8912 \| 0.8254 \| 0.9119 \| \| 0.1187 \| 21.0066 \| 9600 \| 0.7618 \| 0.8260 \| 0.7512 \| 0.9 \| 0.8111 \| 0.9166 \| \| 0.0024 \| 21.2254 \| 9700 \| 0.9334 \| 0.8319 \| 0.8632 \| 0.8010 \| 0.8363 \| 0.9123 \| \| 0.0993 \| 21.4442 \| 9800 \| 0.8067 \| 0.8310 \| 0.8682 \| 0.7941 \| 0.8363 \| 0.9177 \| \| 0.145 \| 21.6630 \| 9900 \| 0.7816 \| 0.8324 \| 0.7770 \| 0.8873 \| 0.8218 \| 0.9108 \| \| 0.054 \| 21.8818 \| 10000 \| 0.8371 \| 0.8413 \| 0.8523 \| 0.8304 \| 0.8423 \| 0.9190 \| \| 0.0446 \| 22.1007 \| 10100 \| 0.8001 \| 0.8354 \| 0.7899 \| 0.8804 \| 0.8268 \| 0.9084 \| \| 0.1218 \| 22.3195 \| 10200 \| 0.8164 \| 0.8364 \| 0.7701 \| 0.9020 \| 0.8240 \| 0.9078 \| \| 0.032 \| 22.5383 \| 10300 \| 0.8353 \| 0.8359 \| 0.8256 \| 0.8461 \| 0.8334 \| 0.9157 \| \| 0.0804 \| 22.7571 \| 10400 \| 0.8301 \| 0.8314 \| 0.7859 \| 0.8765 \| 0.8226 \| 0.9149 \| \| 0.0982 \| 22.9759 \| 10500 \| 0.8366 \| 0.8339 \| 0.8305 \| 0.8373 \| 0.8326 \| 0.9160 \| \| 0.0153 \| 23.1947 \| 10600 \| 0.8395 \| 0.8295 \| 0.7948 \| 0.8637 \| 0.8226 \| 0.9150 \| \| 0.0647 \| 23.4136 \| 10700 \| 0.8342 \| 0.8364 \| 0.8662 \| 0.8069 \| 0.8404 \| 0.9230 \| \| 0.0906 \| 23.6324 \| 10800 \| 0.8414 \| 0.8078 \| 0.8900 \| 0.7265 \| 0.8216 \| 0.9166 \| \| 0.0071 \| 23.8512 \| 10900 \| 0.8552 \| 0.8354 \| 0.7889 \| 0.8814 \| 0.8266 \| 0.9053 \| \| 0.0254 \| 24.0700 \| 11000 \| 0.8612 \| 0.8428 \| 0.7830 \| 0.9020 \| 0.8320 \| 0.9009 \| \| 0.0265 \| 24.2888 \| 11100 \| 1.0379 \| 0.8245 \| 0.7195 \| 0.9284 \| 0.8031 \| 0.8937 \| \| 0.048 \| 24.5077 \| 11200 \| 1.0143 \| 0.8285 \| 0.7611 \| 0.8951 \| 0.8153 \| 0.8942 \| \| 0.0005 \| 24.7265 \| 11300 \| 0.9883 \| 0.8310 \| 0.8077 \| 0.8539 \| 0.8262 \| 0.9024 \| \| 0.1702 \| 24.9453 \| 11400 \| 1.0282 \| 0.8339 \| 0.7512 \| 0.9157 \| 0.8181 \| 0.9078 \| \| 0.0006 \| 25.1641 \| 11500 \| 0.9612 \| 0.8448 \| 0.8712 \| 0.8186 \| 0.8480 \| 0.9151 \| \| 0.0425 \| 25.3829 \| 11600 \| 1.0040 \| 0.8438 \| 0.8612 \| 0.8265 \| 0.8457 \| 0.9143 \| \| 0.0006 \| 25.6018 \| 11700 \| 0.9840 \| 0.8305 \| 0.7790 \| 0.8814 \| 0.8205 \| 0.9117 \| \| 0.0029 \| 25.8206 \| 11800 \| 1.0850 \| 0.8295 \| 0.7294 \| 0.9284 \| 0.8097 \| 0.9039 \| \| 0.0776 \| 26.0394 \| 11900 \| 0.9524 \| 0.8334 \| 0.8335 \| 0.8333 \| 0.8327 \| 0.9119 \| \| 0.0543 \| 26.2582 \| 12000 \| 0.9541 \| 0.8329 \| 0.7572 \| 0.9078 \| 0.8184 \| 0.9097 \| \| 0.0018 \| 26.4770 \| 12100 \| 0.8137 \| 0.8393 \| 0.8712 \| 0.8078 \| 0.8436 \| 0.9225 \| \| 0.0512 \| 26.6958 \| 12200 \| 1.0741 \| 0.8176 \| 0.8712 \| 0.7647 \| 0.8261 \| 0.8886 \| \| 0.0008 \| 26.9147 \| 12300 \| 1.0294 \| 0.8393 \| 0.8484 \| 0.8304 \| 0.8400 \| 0.8987 \| \| 0.043 \| 27.1335 \| 12400 \| 0.9720 \| 0.8334 \| 0.8682 \| 0.7990 \| 0.8383 \| 0.9135 \| \| 0.0013 \| 27.3523 \| 12500 \| 0.9571 \| 0.8374 \| 0.7800 \| 0.8941 \| 0.8267 \| 0.9120 \| \| 0.0163 \| 27.5711 \| 12600 \| 0.9475 \| 0.8305 \| 0.8167 \| 0.8441 \| 0.8273 \| 0.9102 \| \| 0.0034 \| 27.7899 \| 12700 \| 0.8116 \| 0.8403 \| 0.8365 \| 0.8441 \| 0.8390 \| 0.9183 \| \| 0.0014 \| 28.0088 \| 12800 \| 0.9375 \| 0.8305 \| 0.8285 \| 0.8324 \| 0.8294 \| 0.9139 \| \| 0.0008 \| 28.2276 \| 12900 \| 1.0335 \| 0.8314 \| 0.7602 \| 0.9020 \| 0.8177 \| 0.9072 \| \| 0.0497 \| 28.4464 \| 13000 \| 1.0562 \| 0.8285 \| 0.7592 \| 0.8971 \| 0.8149 \| 0.9039 \| \| 0.0319 \| 28.6652 \| 13100 \| 0.7997 \| 0.8364 \| 0.8444 \| 0.8284 \| 0.8369 \| 0.9167 \| \| 0.0932 \| 28.8840 \| 13200 \| 0.8591 \| 0.8167 \| 0.8474 \| 0.7863 \| 0.8213 \| 0.9142 \| \| 0.0007 \| 29.1028 \| 13300 \| 0.8555 \| 0.8379 \| 0.8246 \| 0.8510 \| 0.8349 \| 0.9196 \| \| 0.0025 \| 29.3217 \| 13400 \| 0.9062 \| 0.8359 \| 0.8236 \| 0.8480 \| 0.8331 \| 0.9147 \| \| 0.0117 \| 29.5405 \| 13500 \| 0.8089 \| 0.8339 \| 0.8345 \| 0.8333 \| 0.8333 \| 0.9181 \| \| 0.0505 \| 29.7593 \| 13600 \| 0.9048 \| 0.8329 \| 0.8404 \| 0.8255 \| 0.8334 \| 0.9167 \| \| 0.0484 \| 29.9781 \| 13700 \| 1.0264 \| 0.8265 \| 0.8573 \| 0.7961 \| 0.8309 \| 0.9133 \| \| 0.0004 \| 30.1969 \| 13800 \| 1.0712 \| 0.8349 \| 0.8087 \| 0.8608 \| 0.8297 \| 0.9053 \| \| 0.0157 \| 30.4158 \| 13900 \| 1.0159 \| 0.8236 \| 0.8186 \| 0.8284 \| 0.8219 \| 0.9062 \| \| 0.0004 \| 30.6346 \| 14000 \| 1.0367 \| 0.8305 \| 0.8196 \| 0.8412 \| 0.8278 \| 0.9022 \| \| 0.0003 \| 30.8534 \| 14100 \| 0.9853 \| 0.8314 \| 0.8345 \| 0.8284 \| 0.8312 \| 0.9123 \| \| 0.0039 \| 31.0722 \| 14200 \| 0.9839 \| 0.8413 \| 0.7869 \| 0.8951 \| 0.8314 \| 0.9124 \| \| 0.0505 \| 31.2910 \| 14300 \| 1.0911 \| 0.8339 \| 0.8741 \| 0.7941 \| 0.8396 \| 0.9033 \| \| 0.0007 \| 31.5098 \| 14400 \| 0.8740 \| 0.8374 \| 0.8246 \| 0.85 \| 0.8345 \| 0.9208 \| \| 0.0004 \| 31.7287 \| 14500 \| 0.9801 \| 0.8398 \| 0.8295 \| 0.85 \| 0.8374 \| 0.9208 \| \| 0.0592 \| 31.9475 \| 14600 \| 1.0447 \| 0.8305 \| 0.8404 \| 0.8206 \| 0.8314 \| 0.9165 \| \| 0.0003 \| 32.1663 \| 14700 \| 1.1005 \| 0.8245 \| 0.8543 \| 0.7951 \| 0.8288 \| 0.9129 \| \| 0.0002 \| 32.3851 \| 14800 \| 1.1025 \| 0.8319 \| 0.8176 \| 0.8461 \| 0.8287 \| 0.9108 \| \| 0.0428 \| 32.6039 \| 14900 \| 1.0779 \| 0.8310 \| 0.8236 \| 0.8382 \| 0.8289 \| 0.9096 \| \| 0.049 \| 32.8228 \| 15000 \| 0.9729 \| 0.8408 \| 0.8295 \| 0.8520 \| 0.8383 \| 0.9208 \| \| 0.0219 \| 33.0416 \| 15100 \| 0.9851 \| 0.8211 \| 0.7661 \| 0.8755 \| 0.8098 \| 0.9120 \| \| 0.001 \| 33.2604 \| 15200 \| 0.9834 \| 0.8349 \| 0.8256 \| 0.8441 \| 0.8326 \| 0.9166 \| \| 0.0009 \| 33.4792 \| 15300 \| 1.0128 \| 0.8270 \| 0.7463 \| 0.9069 \| 0.8110 \| 0.9130 \| \| 0.0146 \| 33.6980 \| 15400 \| 0.9835 \| 0.8300 \| 0.7790 \| 0.8804 \| 0.8200 \| 0.9097 \| \| 0.0184 \| 33.9168 \| 15500 \| 0.8922 \| 0.8290 \| 0.8276 \| 0.8304 \| 0.8280 \| 0.9183 \| \| 0.0528 \| 34.1357 \| 15600 \| 0.9727 \| 0.8398 \| 0.7899 \| 0.8892 \| 0.8306 \| 0.9107 \| \| 0.0018 \| 34.3545 \| 15700 \| 1.0313 \| 0.8413 \| 0.8196 \| 0.8627 \| 0.8370 \| 0.9065 \| \| 0.0002 \| 34.5733 \| 15800 \| 1.0882 \| 0.8374 \| 0.7978 \| 0.8765 \| 0.8299 \| 0.9065 \| \| 0.0002 \| 34.7921 \| 15900 \| 1.0866 \| 0.8379 \| 0.8236 \| 0.8520 \| 0.8348 \| 0.9045 \| \| 0.0865 \| 35.0109 \| 16000 \| 1.0595 \| 0.8300 \| 0.7602 \| 0.8990 \| 0.8164 \| 0.8971 \| \| 0.0004 \| 35.2298 \| 16100 \| 1.0287 \| 0.8344 \| 0.7988 \| 0.8696 \| 0.8275 \| 0.9041 \| \| 0.0003 \| 35.4486 \| 16200 \| 1.0652 \| 0.8305 \| 0.8176 \| 0.8431 \| 0.8275 \| 0.8877 \| \| 0.0006 \| 35.6674 \| 16300 \| 1.0627 \| 0.8270 \| 0.7988 \| 0.8549 \| 0.8212 \| 0.8848 \| \| 0.0003 \| 35.8862 \| 16400 \| 1.1173 \| 0.8339 \| 0.7780 \| 0.8892 \| 0.8233 \| 0.8843 \| \| 0.0002 \| 36.1050 \| 16500 \| 1.1114 \| 0.8379 \| 0.8048 \| 0.8706 \| 0.8315 \| 0.8948 \| \| 0.0002 \| 36.3239 \| 16600 \| 1.1165 \| 0.8379 \| 0.8137 \| 0.8618 \| 0.8331 \| 0.8968 \| \| 0.0004 \| 36.5427 \| 16700 \| 1.1693 \| 0.8369 \| 0.8147 \| 0.8588 \| 0.8324 \| 0.8918 \| \| 0.0002 \| 36.7615 \| 16800 \| 1.1609 \| 0.8364 \| 0.8325 \| 0.8402 \| 0.8350 \| 0.8856 \| \| 0.0007 \| 36.9803 \| 16900 \| 1.1993 \| 0.8334 \| 0.8107 \| 0.8559 \| 0.8288 \| 0.8935 \| \| 0.0002 \| 37.1991 \| 17000 \| 1.0206 \| 0.8374 \| 0.8652 \| 0.8098 \| 0.8410 \| 0.9128 \| \| 0.0024 \| 37.4179 \| 17100 \| 0.9984 \| 0.8359 \| 0.7899 \| 0.8814 \| 0.8272 \| 0.9094 \| \| 0.0005 \| 37.6368 \| 17200 \| 1.1162 \| 0.8388 \| 0.7671 \| 0.9098 \| 0.8256 \| 0.8987 \| \| 0.0008 \| 37.8556 \| 17300 \| 0.9434 \| 0.8433 \| 0.8414 \| 0.8451 \| 0.8423 \| 0.9146 \| \| 0.0003 \| 38.0744 \| 17400 \| 0.9508 \| 0.8457 \| 0.8523 \| 0.8392 \| 0.8460 \| 0.9200 \| \| 0.0003 \| 38.2932 \| 17500 \| 1.0299 \| 0.8379 \| 0.8345 \| 0.8412 \| 0.8366 \| 0.9183 \| \| 0.0002 \| 38.5120 \| 17600 \| 1.0518 \| 0.8438 \| 0.8325 \| 0.8549 \| 0.8413 \| 0.9178 \| \| 0.0015 \| 38.7309 \| 17700 \| 1.0205 \| 0.8472 \| 0.8464 \| 0.8480 \| 0.8464 \| 0.9210 \| \| 0.0188 \| 38.9497 \| 17800 \| 1.0644 \| 0.8438 \| 0.7968 \| 0.8902 \| 0.8353 \| 0.9183 \| \| 0.0002 \| 39.1685 \| 17900 \| 1.0497 \| 0.8443 \| 0.8266 \| 0.8618 \| 0.8407 \| 0.9220 \| \| 0.0003 \| 39.3873 \| 18000 \| 1.0802 \| 0.8443 \| 0.8236 \| 0.8647 \| 0.8402 \| 0.9210 \| \| 0.0002 \| 39.6061 \| 18100 \| 1.1465 \| 0.8393 \| 0.7958 \| 0.8824 \| 0.8313 \| 0.9186 \| \| 0.0002 \| 39.8249 \| 18200 \| 1.0551 \| 0.8467 \| 0.8147 \| 0.8784 \| 0.8409 \| 0.9185 \| \| 0.0002 \| 40.0438 \| 18300 \| 1.0791 \| 0.8467 \| 0.8147 \| 0.8784 \| 0.8409 \| 0.9171 \| \| 0.0002 \| 40.2626 \| 18400 \| 1.0902 \| 0.8487 \| 0.8176 \| 0.8794 \| 0.8431 \| 0.9175 \| \| 0.0002 \| 40.4814 \| 18500 \| 1.1028 \| 0.8487 \| 0.8176 \| 0.8794 \| 0.8431 \| 0.9175 \| \| 0.0001 \| 40.7002 \| 18600 \| 1.1156 \| 0.8487 \| 0.8176 \| 0.8794 \| 0.8431 \| 0.9165 \| \| 0.0001 \| 40.9190 \| 18700 \| 1.1266 \| 0.8487 \| 0.8176 \| 0.8794 \| 0.8431 \| 0.9168 \| \| 0.0002 \| 41.1379 \| 18800 \| 1.0527 \| 0.8472 \| 0.8246 \| 0.8696 \| 0.8430 \| 0.9186 \| \| 0.0002 \| 41.3567 \| 18900 \| 1.0758 \| 0.8477 \| 0.8226 \| 0.8725 \| 0.8431 \| 0.9190 \| \| 0.0001 \| 41.5755 \| 19000 \| 1.0940 \| 0.8492 \| 0.8216 \| 0.8765 \| 0.8442 \| 0.9199 \| \| 0.0268 \| 41.7943 \| 19100 \| 0.9887 \| 0.8374 \| 0.8494 \| 0.8255 \| 0.8386 \| 0.9196 \| \| 0.002 \| 42.0131 \| 19200 \| 1.0890 \| 0.8354 \| 0.7730 \| 0.8971 \| 0.8237 \| 0.9172 \| \| 0.0002 \| 42.2319 \| 19300 \| 1.0668 \| 0.8418 \| 0.8147 \| 0.8686 \| 0.8366 \| 0.9154 \| \| 0.0001 \| 42.4508 \| 19400 \| 1.1239 \| 0.8383 \| 0.7899 \| 0.8863 \| 0.8293 \| 0.9150 \| \| 0.0001 \| 42.6696 \| 19500 \| 1.1372 \| 0.8364 \| 0.8285 \| 0.8441 \| 0.8343 \| 0.9084 \| \| 0.0001 \| 42.8884 \| 19600 \| 1.1153 \| 0.8393 \| 0.7869 \| 0.8912 \| 0.8297 \| 0.9200 \| \| 0.0001 \| 43.1072 \| 19700 \| 1.1482 \| 0.8413 \| 0.7790 \| 0.9029 \| 0.8300 \| 0.9184 \| \| 0.0001 \| 43.3260 \| 19800 \| 1.1535 \| 0.8388 \| 0.7859 \| 0.8912 \| 0.8291 \| 0.9180 \| \| 0.0001 \| 43.5449 \| 19900 \| 1.1138 \| 0.8393 \| 0.8236 \| 0.8549 \| 0.8360 \| 0.9188 \| \| 0.0001 \| 43.7637 \| 20000 \| 1.1321 \| 0.8393 \| 0.8186 \| 0.8598 \| 0.8352 \| 0.9176 \| \| 0.0001 \| 43.9825 \| 20100 \| 1.1473 \| 0.8403 \| 0.8147 \| 0.8657 \| 0.8354 \| 0.9163 \| \| 0.0001 \| 44.2013 \| 20200 \| 1.1550 \| 0.8413 \| 0.8137 \| 0.8686 \| 0.8360 \| 0.9154 \| \| 0.0001 \| 44.4201 \| 20300 \| 1.1630 \| 0.8428 \| 0.8127 \| 0.8725 \| 0.8372 \| 0.9143 \| \| 0.0001 \| 44.6389 \| 20400 \| 1.1718 \| 0.8428 \| 0.8117 \| 0.8735 \| 0.8370 \| 0.9133 \| \| 0.0001 \| 44.8578 \| 20500 \| 1.1793 \| 0.8428 \| 0.8117 \| 0.8735 \| 0.8370 \| 0.9129 \| \| 0.0001 \| 45.0766 \| 20600 \| 1.1869 \| 0.8418 \| 0.8097 \| 0.8735 \| 0.8358 \| 0.9121 \| \| 0.0001 \| 45.2954 \| 20700 \| 1.1931 \| 0.8413 \| 0.8087 \| 0.8735 \| 0.8352 \| 0.9115 \| \| 0.0001 \| 45.5142 \| 20800 \| 1.1990 \| 0.8418 \| 0.8097 \| 0.8735 \| 0.8358 \| 0.9103 \| \| 0.0001 \| 45.7330 \| 20900 \| 1.2056 \| 0.8418 \| 0.8087 \| 0.8745 \| 0.8356 \| 0.9097 \| \| 0.0001 \| 45.9519 \| 21000 \| 1.2116 \| 0.8423 \| 0.8087 \| 0.8755 \| 0.8361 \| 0.9092 \| \| 0.0001 \| 46.1707 \| 21100 \| 1.2176 \| 0.8428 \| 0.8087 \| 0.8765 \| 0.8365 \| 0.9090 \| \| 0.0001 \| 46.3895 \| 21200 \| 1.2233 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9082 \| \| 0.0001 \| 46.6083 \| 21300 \| 1.2281 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9079 \| \| 0.0001 \| 46.8271 \| 21400 \| 1.2322 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9075 \| \| 0.0001 \| 47.0460 \| 21500 \| 1.2365 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9075 \| \| 0.0001 \| 47.2648 \| 21600 \| 1.2402 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9074 \| \| 0.0001 \| 47.4836 \| 21700 \| 1.2447 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9060 \| \| 0.0001 \| 47.7024 \| 21800 \| 1.2484 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9068 \| \| 0.0001 \| 47.9212 \| 21900 \| 1.2516 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9064 \| \| 0.0 \| 48.1400 \| 22000 \| 1.2546 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9068 \| \| 0.0 \| 48.3589 \| 22100 \| 1.2572 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9062 \| \| 0.0 \| 48.5777 \| 22200 \| 1.2603 \| 0.8433 \| 0.8087 \| 0.8775 \| 0.8369 \| 0.9058 \| \| 0.0 \| 48.7965 \| 22300 \| 1.2628 \| 0.8438 \| 0.8087 \| 0.8784 \| 0.8374 \| 0.9057 \| \| 0.0 \| 49.0153 \| 22400 \| 1.2647 \| 0.8438 \| 0.8087 \| 0.8784 \| 0.8374 \| 0.9053 \| \| 0.0 \| 49.2341 \| 22500 \| 1.2663 \| 0.8438 \| 0.8087 \| 0.8784 \| 0.8374 \| 0.9055 \| \| 0.0 \| 49.4530 \| 22600 \| 1.2679 \| 0.8438 \| 0.8087 \| 0.8784 \| 0.8374 \| 0.9058 \| \| 0.0 \| 49.6718 \| 22700 \| 1.2687 \| 0.8438 \| 0.8087 \| 0.8784 \| 0.8374 \| 0.9057 \| \| 0.0 \| 49.8906 \| 22800 \| 1.2691 \| 0.8438 \| 0.8087 \| 0.8784 \| 0.8374 \| 0.9061 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 2.20.0 - Tokenizers 0.21.0
AustinCarthy/MixGPT2_Domain_100KP_BFall_fromP_50K_topP_0.75_ratio5	AustinCarthy	"2023-06-09T15:14:02Z"	0	0	null	[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	"2023-06-09T12:05:42Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2_Domain_100KP_BFall_fromP_50K_topP_0.75_ratio5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MixGPT2_Domain_100KP_BFall_fromP_50K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2_using_phish_50K_top_p_0.75_domain dataset. It achieves the following results on the evaluation set: - Loss: 0.0199 - Accuracy: 0.9980 - F1: 0.9782 - Precision: 0.9990 - Recall: 0.9582 - Roc Auc Score: 0.9791 - Tpr At Fpr 0.01: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.0058 \| 1.0 \| 28125 \| 0.0175 \| 0.9968 \| 0.9656 \| 0.9977 \| 0.9356 \| 0.9677 \| 0.931 \| \| 0.002 \| 2.0 \| 56250 \| 0.0137 \| 0.9978 \| 0.9769 \| 0.9979 \| 0.9568 \| 0.9783 \| 0.9562 \| \| 0.0015 \| 3.0 \| 84375 \| 0.0197 \| 0.9976 \| 0.9741 \| 0.9977 \| 0.9516 \| 0.9757 \| 0.9482 \| \| 0.0006 \| 4.0 \| 112500 \| 0.0174 \| 0.9976 \| 0.9739 \| 0.9983 \| 0.9506 \| 0.9753 \| 0.9528 \| \| 0.0 \| 5.0 \| 140625 \| 0.0199 \| 0.9980 \| 0.9782 \| 0.9990 \| 0.9582 \| 0.9791 \| 0.96 \| ### Framework versions - Transformers 4.29.1 - Pytorch 1.9.0+cu111 - Datasets 2.10.1 - Tokenizers 0.13.2
dima806/multiple_accent_classification	dima806	"2024-10-19T10:58:20Z"	5	1	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "audio-classification", "base_model:facebook/wav2vec2-base-960h", "base_model:finetune:facebook/wav2vec2-base-960h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	"2023-08-18T18:19:06Z"	--- license: apache-2.0 metrics: - accuracy - roc_auc base_model: - facebook/wav2vec2-base-960h --- More details in https://www.kaggle.com/code/dima806/speech-accent-multiclass-classification
MichalMlodawski/open-closed-eye-classification-mobilevitv2-1.0	MichalMlodawski	"2024-09-20T10:50:03Z"	191	1	transformers	[ "transformers", "pytorch", "safetensors", "mobilevitv2", "image-classification", "eye", "eyes", "en", "dataset:MichalMlodawski/closed-open-eyes", "license:cc-by-nc-nd-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-07-22T06:15:17Z"	--- license: cc-by-nc-nd-4.0 datasets: - MichalMlodawski/closed-open-eyes language: - en tags: - eye - eyes model-index: - name: mobilevitv2 Eye State Classifier results: - task: type: image-classification dataset: name: MichalMlodawski/closed-open-eyes type: custom metrics: - name: Accuracy type: self-reported value: 99% - name: Precision type: self-reported value: 99% - name: Recall type: self-reported value: 99% --- --- # 👁️ Open-Closed Eye Classification mobilevitv2 👁️ ## Model Overview 🔍 This model is a fine-tuned version of mobilevitv2, specifically designed for classifying images of eyes as either open or closed. With an impressive accuracy of 99%, this classifier excels in distinguishing between open and closed eyes in various contexts. ## Model Details 📊 - Model Name: open-closed-eye-classification-focalnet-base - Base Model: apple/mobilevitv2-1.0-imagenet1k-256 - Fine-tuned By: Michał Młodawski - Categories: - 0: Closed Eyes 😴 - 1: Open Eyes 👀 - Accuracy: 99% 🎯 ## Use Cases 💡 This high-accuracy model is particularly useful for applications involving: - Driver Drowsiness Detection 🚗 - Attentiveness Monitoring in Educational Settings 🏫 - Medical Diagnostics related to Eye Conditions 🏥 - Facial Analysis in Photography and Videography 📸 - Human-Computer Interaction Systems 💻 ## How It Works 🛠️ The model takes an input image and classifies it into one of two categories: - Closed Eyes (0): Images where the subject's eyes are fully or mostly closed. - Open Eyes (1): Images where the subject's eyes are open. The classification leverages the advanced image processing capabilities of the FocalNet architecture, fine-tuned on a carefully curated dataset of eye images. ## Getting Started 🚀 To start using the open-closed-eye-classification-focalnet-base, you can integrate it into your projects with the following steps: ### Installation ```bash pip install transformers==4.37.2 pip install torch==2.3.1 pip install Pillow ``` ### Usage ```python import os from PIL import Image import torch from torchvision import transforms from transformers import AutoImageProcessor, MobileViTV2ForImageClassification # Path to the folder with images image_folder = "" # Path to the model model_path = "MichalMlodawski/open-closed-eye-classification-mobilevitv2-1.0" # List of jpg files in the folder jpg_files = [file for file in os.listdir(image_folder) if file.lower().endswith(".jpg")] # Check if there are jpg files in the folder if not jpg_files: print("🚫 No jpg files found in folder:", image_folder) exit() # Load the model and image processor image_processor = AutoImageProcessor.from_pretrained(model_path) model = MobileViTV2ForImageClassification.from_pretrained(model_path) model.eval() # Image transformations transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor() ]) # Processing and prediction for each image results = [] for jpg_file in jpg_files: selected_image = os.path.join(image_folder, jpg_file) image = Image.open(selected_image).convert("RGB") image_tensor = transform(image).unsqueeze(0) # Process image using image_processor inputs = image_processor(images=image, return_tensors="pt") # Prediction using the model with torch.no_grad(): outputs = model(*inputs) probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) confidence, predicted = torch.max(probabilities, 1) results.append((jpg_file, predicted.item(), confidence.item() 100)) # Display results print("🖼️ Image Classification Results 🖼️") print("=" * 40) for jpg_file, prediction, confidence in results: emoji = "👁️" if prediction == 1 else "❌" confidence_bar = "🟩" * int(confidence // 10) + "⬜" * (10 - int(confidence // 10)) print(f"📄 File name: {jpg_file}") print(f"{emoji} Prediction: {'Open' if prediction == 1 else 'Closed'}") print(f"🎯 Confidence: {confidence:.2f}% {confidence_bar}") print(f"{'=' * 40}") print("🏁 Classification completed! 🎉") ``` ## Disclaimer ⚠️ This model is provided for research and development purposes only. The creators and distributors of this model do not assume any legal responsibility for its use or misuse. Users are solely responsible for ensuring that their use of this model complies with applicable laws, regulations, and ethical standards. The model's performance may vary depending on the quality and nature of input images. Always validate results in critical applications. 🚫 Do not use this model for any illegal, unethical, or potentially harmful purposes. 📝 Please note that while the model demonstrates high accuracy, it should not be used as a sole decision-making tool in safety-critical systems without proper validation and human oversight.
Piotrasz/Llama-2-7b-hf-ROME-50-en-32b	Piotrasz	"2024-06-18T14:56:38Z"	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-18T14:30:19Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gajanhcc/finetune_colpali_v1_2-ownep6	gajanhcc	"2025-03-19T07:03:40Z"	0	0	transformers	[ "transformers", "safetensors", "colpali", "generated_from_trainer", "base_model:vidore/colpaligemma-3b-pt-448-base", "base_model:finetune:vidore/colpaligemma-3b-pt-448-base", "license:gemma", "endpoints_compatible", "region:us" ]	null	"2025-03-19T07:03:32Z"	--- library_name: transformers license: gemma base_model: vidore/colpaligemma-3b-pt-448-base tags: - colpali - generated_from_trainer model-index: - name: finetune_colpali_v1_2-ownep6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetune_colpali_v1_2-ownep6 This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the gajanhcc/fashion-query-dataset-10samples dataset. It achieves the following results on the evaluation set: - Loss: 0.0020 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0.01 \| 1 \| 0.2341 \| \| 0.0068 \| 1.0 \| 100 \| 0.0022 \| \| 0.0 \| 2.0 \| 200 \| 0.0038 \| \| 0.0 \| 3.0 \| 300 \| 0.0036 \| \| 0.0001 \| 4.0 \| 400 \| 0.0008 \| \| 0.0018 \| 5.0 \| 500 \| 0.0015 \| \| 0.0002 \| 6.0 \| 600 \| 0.0020 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
rafoo1337/alex	rafoo1337	"2025-03-11T14:44:35Z"	0	0	null	[ "license:other", "region:us" ]	null	"2025-03-11T14:05:56Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
nick808p/nicklone	nick808p	"2025-01-20T06:17:11Z"	14	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-01-20T05:55:59Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: nicklone --- # Nicklone <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nicklone` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('nick808p/nicklone', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
ehab215/AnemiaTypePrediction	ehab215	"2024-05-06T18:03:01Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-05-06T18:01:48Z"	--- license: apache-2.0 ---
automerger/Experiment27Experiment28-7B	automerger	"2024-03-11T10:05:04Z"	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:yam-peleg/Experiment27-7B", "base_model:merge:yam-peleg/Experiment27-7B", "base_model:yam-peleg/Experiment28-7B", "base_model:merge:yam-peleg/Experiment28-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-03-11T10:04:01Z"	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - yam-peleg/Experiment27-7B - yam-peleg/Experiment28-7B --- # Experiment27Experiment28-7B Experiment27Experiment28-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [yam-peleg/Experiment27-7B](https://huggingface.co/yam-peleg/Experiment27-7B) * [yam-peleg/Experiment28-7B](https://huggingface.co/yam-peleg/Experiment28-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: yam-peleg/Experiment27-7B layer_range: [0, 32] - model: yam-peleg/Experiment28-7B layer_range: [0, 32] merge_method: slerp base_model: yam-peleg/Experiment27-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment27Experiment28-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
ymlee/test	ymlee	"2024-05-31T04:11:09Z"	91	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-05-31T04:10:11Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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manuvallejos/loratrainingmnaindez	manuvallejos	"2024-10-02T01:47:31Z"	40	1	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2024-10-02T01:25:12Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Loratrainingmnaindez <!-- <Gallery /> --> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('manuvallejos/loratrainingmnaindez', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
zhilong777/csllm	zhilong777	"2025-03-13T06:30:43Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-03-13T06:30:43Z"	--- license: apache-2.0 ---
squarelike/Gugugo-koen-7B-V1.1	squarelike	"2024-03-29T06:02:43Z"	298	17	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "translation", "en", "ko", "dataset:squarelike/sharegpt_deepl_ko_translation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	"2023-10-27T14:38:43Z"	--- license: apache-2.0 datasets: - squarelike/sharegpt_deepl_ko_translation language: - en - ko pipeline_tag: translation --- # Gugugo-koen-7B-V1.1 Detail repo: [https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo) ![Gugugo](./logo.png) Base Model: [Llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) Training Dataset: [sharegpt_deepl_ko_translation](https://huggingface.co/datasets/squarelike/sharegpt_deepl_ko_translation). I trained with 1x A6000 GPUs for 90 hours. ## Prompt Template KO->EN ``` ### 한국어: {sentence}</끝> ### 영어: ``` EN->KO ``` ### 영어: {sentence}</끝> ### 한국어: ``` There are GPTQ, AWQ, and GGUF support. [https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-GPTQ](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-GPTQ) [https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-AWQ](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-AWQ) [https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-GGUF](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-GGUF) ## Implementation Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList import torch repo = "squarelike/Gugugo-koen-7B-V1.1" model = AutoModelForCausalLM.from_pretrained( repo, load_in_4bit=True device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(repo) class StoppingCriteriaSub(StoppingCriteria): def __init__(self, stops = [], encounters=1): super().__init__() self.stops = [stop for stop in stops] def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor): for stop in self.stops: if torch.all((stop == input_ids[0][-len(stop):])).item(): return True return False stop_words_ids = torch.tensor([[829, 45107, 29958], [1533, 45107, 29958], [829, 45107, 29958], [21106, 45107, 29958]]).to("cuda") stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)]) def gen(lan="en", x=""): if (lan == "ko"): prompt = f"### 한국어: {x}</끝>\n### 영어:" else: prompt = f"### 영어: {x}</끝>\n### 한국어:" gened = model.generate( **tokenizer( prompt, return_tensors='pt', return_token_type_ids=False ).to("cuda"), max_new_tokens=2000, temperature=0.3, # no_repeat_ngram_size=5, num_beams=5, stopping_criteria=stopping_criteria ) return tokenizer.decode(gened[0][1:]).replace(prompt+" ", "").replace("</끝>", "") print(gen(lan="en", x="Hello, world!")) ```
JvThunder/a2c-AntBulletEnv-v0	JvThunder	"2023-07-20T09:02:28Z"	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-06-17T19:07:25Z"	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1480.48 +/- 111.99 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Helsinki-NLP/opus-mt-ty-fr	Helsinki-NLP	"2023-08-16T12:07:56Z"	108	0	transformers	[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "ty", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	"2022-03-02T23:29:04Z"	--- tags: - translation license: apache-2.0 --- ### opus-mt-ty-fr * source languages: ty * target languages: fr * OPUS readme: [ty-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ty-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ty-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ty-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ty-fr/opus-2020-01-16.eval.txt) ## Benchmarks \| testset \| BLEU \| chr-F \| \|-----------------------\|-------\|-------\| \| JW300.ty.fr \| 30.2 \| 0.480 \|
tahaenesaslanturk/ts-corpus-bpe-256k-cased	tahaenesaslanturk	"2024-04-30T22:39:04Z"	0	0	null	[ "license:mit", "region:us" ]	null	"2024-04-30T22:36:56Z"	--- license: mit --- # TS-Corpus BPE Tokenizer (256k, Cased) ## Overview This repository hosts a Byte Pair Encoding (BPE) tokenizer with a vocabulary size of 256,000, trained cased using several datasets from the TS Corpus website. The BPE method is particularly effective for languages like Turkish, providing a balance between word-level and character-level tokenization. ## Dataset Sources The tokenizer was trained on a variety of text sources from TS Corpus, ensuring a broad linguistic coverage. These sources include: - [TS Corpus V2](https://tscorpus.com/corpora/ts-corpus-v2/) - [TS Wikipedia Corpus](https://tscorpus.com/corpora/ts-wikipedia-corpus/) - [TS Abstract Corpus](https://tscorpus.com/corpora/ts-abstract-corpus/) - [TS Idioms and Proverbs Corpus](https://tscorpus.com/corpora/ts-idioms-and-proverbs-corpus/) - [Syllable Corpus](https://tscorpus.com/corpora/syllable-corpus/) - [Turkish Constitution Corpus](https://tscorpus.com/corpora/turkish-constitution-corpus/) The inclusion of idiomatic expressions, proverbs, and legal terminology provides a comprehensive toolkit for processing Turkish text across different domains. ## Tokenizer Model Utilizing the Byte Pair Encoding (BPE) method, this tokenizer excels in efficiently managing subword units without the need for an extensive vocabulary. BPE is especially suitable for handling the agglutinative nature of Turkish, where words can have multiple suffixes. ## Usage To use this tokenizer in your projects, load it with the Hugging Face `transformers` library: ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("tahaenesaslanturk/ts-corpus-bpe-256k-cased") ```
appohfaiths/ghanaba-distilbert-gpt2-edu-med	appohfaiths	"2025-01-13T10:02:39Z"	23	0	null	[ "safetensors", "gpt2", "text-generation", "en", "arxiv:1910.09700", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "region:us" ]	text-generation	"2025-01-13T08:15:50Z"	--- license: apache-2.0 language: - en base_model: - distilbert/distilgpt2 pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Gyaneshere/ppo-PyramidsTraining	Gyaneshere	"2025-02-06T06:43:49Z"	15	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	"2025-02-06T06:43:40Z"	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Gyaneshere/ppo-PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀

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