🚧 wip docs

hfdocs/source/index.mdx

@@ -2,92 +2,11 @@
 
 ## Welcome
 
-`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation scripts.
+`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation scripts. It provides high-level APIs for popular model architectures and standard datasets, as well as utilities for low-level model manipulation.
 
-TODO - clean up, move code snippets to quickstart
+```python
+import timm
 
-Welcome to the `timm` documentation, a lean set of docs that covers the basics of `timm`.
-
-For a more comprehensive set of docs (currently under development), please visit [timmdocs](http://timm.fast.ai) by [Aman Arora](https://github.com/amaarora).
-
-## Install
-
-The library can be installed with pip:
-
-```
-pip install timm
-```
-
-I update the PyPi (pip) packages when I'm confident there are no significant model regressions from previous releases. If you want to pip install the bleeding edge from GitHub, use:
-```
-pip install git+https://github.com/rwightman/pytorch-image-models.git
-```
-
-### Conda Environment
-
-<Tip>
-
-- All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically 3.7, 3.8, 3.9, 3.10
-
-- Little to no care has been taken to be Python 2.x friendly and will not support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment.
-
-- PyTorch versions 1.9, 1.10, 1.11 have been tested with the latest versions of this code.
-
-</Tip>
-
-I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda:
-
-```bash
-conda create -n torch-env
-conda activate torch-env
-conda install pytorch torchvision cudatoolkit=11.3 -c pytorch
-conda install pyyaml
-```
-
-## Load a Pretrained Model
-
-Pretrained models can be loaded using `timm.create_model`
-
-```py
->>> import timm
-
->>> m = timm.create_model('mobilenetv3_large_100', pretrained=True)
->>> m.eval()
-```
-
-## List Models with Pretrained Weights
-
-```py
->>> import timm
->>> from pprint import pprint
->>> model_names = timm.list_models(pretrained=True)
->>> pprint(model_names)
-[
-    'adv_inception_v3',
-    'cspdarknet53',
-    'cspresnext50',
-    'densenet121',
-    'densenet161',
-    'densenet169',
-    'densenet201',
-    'densenetblur121d',
-    'dla34',
-    'dla46_c',
-]
-```
-
-## List Model Architectures by Wildcard
-
-```py
->>> import timm
->>> from pprint import pprint
->>> model_names = timm.list_models('*resne*t*')
->>> pprint(model_names)
-[
-    'cspresnet50',
-    'cspresnet50d',
-    'cspresnet50w',
-    'cspresnext50',
-    ...
-]
-```
+# Load a Pretrained Model
+model = timm.create_model('resnet18', pretrained=True) 
+```

hfdocs/source/quickstart.mdx

@@ -1,3 +1,49 @@
 # Quickstart
 
-TODO
+## Load a Pretrained Model
+
+Pretrained models can be loaded using `timm.create_model`
+
+```py
+>>> import timm
+
+>>> m = timm.create_model('mobilenetv3_large_100', pretrained=True)
+>>> m.eval()
+```
+
+## List Models with Pretrained Weights
+
+```py
+>>> import timm
+>>> from pprint import pprint
+>>> model_names = timm.list_models(pretrained=True)
+>>> pprint(model_names)
+[
+    'adv_inception_v3',
+    'cspdarknet53',
+    'cspresnext50',
+    'densenet121',
+    'densenet161',
+    'densenet169',
+    'densenet201',
+    'densenetblur121d',
+    'dla34',
+    'dla46_c',
+]
+```
+
+## List Model Architectures by Wildcard
+
+```py
+>>> import timm
+>>> from pprint import pprint
+>>> model_names = timm.list_models('*resne*t*')
+>>> pprint(model_names)
+[
+    'cspresnet50',
+    'cspresnet50d',
+    'cspresnet50w',
+    'cspresnext50',
+    ...
+]
+```