📝 wip docs

hfdocs/source/_toctree.yml

@@ -9,36 +9,14 @@
 - sections:
   - local: inference
     title: Using Pretrained Models for Inference
+  - local: feature_extraction
+    title: Using Pretrained Models as Feature Extractors
   - local: training_script
     title: Training With The Official Training Script
   - local: hf_hub
     title: Share and Load Models from the Hugging Face Hub
   title: Tutorials
 - sections:
-  - local: reference/models
-    title: Models
-  - local: reference/augmentation
-    title: Augmentation
-  - local: reference/data
-    title: Data
-  - local: reference/optimization
-    title: Optimization
-  title: Reference
-- sections:
-  - local: models
-    title: Model Summaries
-  - local: results
-    title: Results
-  - local: scripts
-    title: Scripts
-  - local: training_hparam_examples
-    title: Training Examples
-  - local: feature_extraction
-    title: Feature Extraction
-  - local: changes
-    title: Recent Changes
-  - local: archived_changes
-    title: Archived Changes
   - local: model_pages
     title: Model Pages
     isExpanded: false
@@ -169,4 +147,28 @@
         title: Wide ResNet
       - local: models/xception
         title: Xception
+  title: Available Models
+- sections:
+  - local: reference/models
+    title: Models
+  - local: reference/augmentation
+    title: Augmentation
+  - local: reference/data
+    title: Data
+  - local: reference/optimization
+    title: Optimization
+  title: Reference
+- sections:
+  - local: models
+    title: Model Summaries
+  - local: results
+    title: Results
+  - local: scripts
+    title: Scripts
+  - local: training_hparam_examples
+    title: Training Examples
+  - local: changes
+    title: Recent Changes
+  - local: archived_changes
+    title: Archived Changes
   title: Legacy Docs

hfdocs/source/quickstart.mdx

@@ -1,5 +1,13 @@
 # Quickstart
 
+This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate `timm` into their model training workflow.
+
+First, you'll need to install `timm`. For more information on installation, see [Installation](installation).
+
+```bash
+pip install timm
+```
+
 ## Load a Pretrained Model
 
 Pretrained models can be loaded using `timm.create_model`
@@ -11,8 +19,36 @@ Pretrained models can be loaded using `timm.create_model`
 >>> m.eval()
 ```
 
+## Fine-Tune a Pretrained Model
+
+You can finetune any of the pre-trained models just by changing the classifier (the last layer).
+
+```py
+>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
+```
+
+To fine-tune on your own dataset, you have to write a PyTorch training loop or adapt `timm`'s [training script](training_script) to use your dataset.
+
+## Use a Pretrained Model for Feature Extraction
+
+Without modifying the network, one can call model.forward_features(input) on any model instead of the usual model(input). This will bypass the head classifier and global pooling for networks.
+
+For a more in depth guide to using `timm` for feature extraction, see [Feature Extraction](feature_extraction).
+
+```py
+>>> import timm
+>>> import torch
+>>> x = torch.randn(1, 3, 224, 224)
+>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True)
+>>> features = model.forward_features(x)
+>>> print(features.shape)
+torch.Size([1, 960, 7, 7])
+```
+
 ## List Models with Pretrained Weights
 
+You can list all models with pretrained weights using `timm.list_models`.
+
 ```py
 >>> import timm
 >>> from pprint import pprint
@@ -32,7 +68,7 @@ Pretrained models can be loaded using `timm.create_model`
 ]
 ```
 
-## List Model Architectures by Wildcard
+You can also list models with a specific pattern in their name.
 
 ```py
 >>> import timm