Upload README.md with huggingface_hub

README.md

@@ -12,36 +12,15 @@ tags:
 - annotated:False
 ---
 
-
-ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts.
-The learned low-dimensional latent representation of the data can be used for visualization and clustering.
-
-scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
-We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html).
-
-- See our original manuscript for further details of the model: [scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
-- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how to leverage pre-trained models.
-
-This model can be used for fine tuning on new data using our Arches framework: [Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html).
-
-
-# Model Description
+# Description
 
 scVI model trained on synthetic IID data and uploaded with the minified data.
 
-# Model Properties
-
-We provide here key parameters used to setup and train the model.
+# Model properties
 
-<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
-sure to provide this field if you want users to be able to access your training data. See the
-scvi-tools documentation for details. -->
-**Training data url**: N/A
+Many model properties are in the model tags. Some more are listed below.
 
-<details>
-<summary><strong>Model Parameters</strong></summary>
-
-These provide the settings to setup the original model:
+**model_init_params**:
 ```json
 {
     "n_hidden": 128,
@@ -54,12 +33,7 @@ These provide the settings to setup the original model:
 }
 ```
 
-</details>
-
-<details>
-<summary><strong>Setup Data Arguments</strong></summary>
-
-Arguments passed to setup_anndata of the original model:
+**model_setup_anndata_args**:
 ```json
 {
     "layer": null,
@@ -71,12 +45,19 @@ Arguments passed to setup_anndata of the original model:
 }
 ```
 
-</details>
-
-<details>
-<summary><strong>Data Registry</strong></summary>
+**model_summary_stats**:
+|     Summary Stat Key     | Value |
+|--------------------------|-------|
+|         n_batch          |   1   |
+|         n_cells          |  400  |
+| n_extra_categorical_covs |   0   |
+| n_extra_continuous_covs  |   0   |
+|         n_labels         |   1   |
+|       n_latent_qzm       |  10   |
+|       n_latent_qzv       |  10   |
+|          n_vars          |  100  |
 
-Registry elements for AnnData manager:
+**model_data_registry**:
 |   Registry Key    |         scvi-tools Location          |
 |-------------------|--------------------------------------|
 |         X         |               adata.X                |
@@ -87,36 +68,27 @@ Registry elements for AnnData manager:
 |    minify_type    | adata.uns['_scvi_adata_minify_type'] |
 | observed_lib_size |    adata.obs['observed_lib_size']    |
 
-- **Data is Minified**: True
+**model_parent_module**: scvi.model
 
-</details>
+**data_is_minified**: True
 
-<details>
-<summary><strong>Summary Statistics</strong></summary>
+# Training data
 
-|     Summary Stat Key     | Value |
-|--------------------------|-------|
-|         n_batch          |   1   |
-|         n_cells          |  400  |
-| n_extra_categorical_covs |   0   |
-| n_extra_continuous_covs  |   0   |
-|         n_labels         |   1   |
-|       n_latent_qzm       |  10   |
-|       n_latent_qzv       |  10   |
-|          n_vars          |  100  |
-
-</details>
+This is an optional link to where the training data is stored if it is too large
+to host on the huggingface Model hub.
 
+<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
+sure to provide this field if you want users to be able to access your training data. See the
+scvi-tools documentation for details. -->
 
-<details>
-<summary><strong>Training</strong></summary>
+Training data url: N/A
 
-If provided by the original uploader, for those interested in understanding or replicating the training process, the code is available at the link below.
+# Training code
 
-**Training Code URL**: N/A
+This is an optional link to the code used to train the model.
 
-</details>
+Training code url: N/A
 
 # References
 
-To be added...
+To be added...