diff --git a/docs/Pytorch-Lightning/LightningModule.md b/docs/LightningModule/RequiredTrainerInterface.md
similarity index 92%
rename from docs/Pytorch-Lightning/LightningModule.md
rename to docs/LightningModule/RequiredTrainerInterface.md
index 79e02029..465ce2fc 100644
--- a/docs/Pytorch-Lightning/LightningModule.md
+++ b/docs/LightningModule/RequiredTrainerInterface.md
@@ -1,4 +1,4 @@
-# Lightning module  
+# Lightning Module interface
 [[Github Code](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/root_module.py)]
 
 A lightning module is a strict superclass of nn.Module, it provides a standard interface for the trainer to interact with the model.
@@ -9,22 +9,22 @@ Otherwise, to Define a Lightning Module, implement the following methods:
 
 **Required**:  
 
-- [training_step](LightningModule.md#training_step)   
-- [validation_step](LightningModule.md#validation_step)
-- [validation_end](LightningModule.md#validation_end)
+- [training_step](RequiredTrainerInterface.md#training_step)   
+- [validation_step](RequiredTrainerInterface.md#validation_step)
+- [validation_end](RequiredTrainerInterface.md#validation_end)
     
-- [configure_optimizers](LightningModule.md#configure_optimizers)
-- [get_save_dict](LightningModule.md#get_save_dict)
-- [load_model_specific](LightningModule.md#load_model_specific)
+- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)
+- [get_save_dict](RequiredTrainerInterface.md#get_save_dict)
+- [load_model_specific](RequiredTrainerInterface.md#load_model_specific)
 
-- [tng_dataloader](LightningModule.md#tng_dataloader)
-- [tng_dataloader](LightningModule.md#tng_dataloader)
-- [test_dataloader](LightningModule.md#test_dataloader)
+- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
+- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
+- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)
 
 **Optional**:   
 
-- [update_tng_log_metrics](LightningModule.md#update_tng_log_metrics)
-- [add_model_specific_args](LightningModule.md#add_model_specific_args)
+- [update_tng_log_metrics](RequiredTrainerInterface.md#update_tng_log_metrics)
+- [add_model_specific_args](RequiredTrainerInterface.md#add_model_specific_args)
 
 ---
 
@@ -391,4 +391,4 @@ def add_model_specific_args(parent_parser, root_dir):
     parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
     parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
     return parser
-```
+```
\ No newline at end of file
diff --git a/docs/LightningModule/methods.md b/docs/LightningModule/methods.md
new file mode 100644
index 00000000..9163326e
--- /dev/null
+++ b/docs/LightningModule/methods.md
@@ -0,0 +1,49 @@
+Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.
+
+---    
+### freeze 
+Freeze all params for inference
+```{.python}
+model = MyLightningModule(...)
+model.freeze()
+```
+
+---    
+### load_from_metrics
+This is the easiest/fastest way which uses the meta_tags.csv file from test-tube to rebuild the model.
+The meta_tags.csv file can be found in the test-tube experiment save_dir.       
+
+```{.python}
+pretrained_model = MyLightningModule.load_from_metrics(
+    weights_path='/path/to/pytorch_checkpoint.ckpt',
+    tags_csv='/path/to/test_tube/experiment/version/meta_tags.csv',
+    on_gpu=True,
+    map_location=None
+)
+    
+# predict 
+pretrained_model.freeze()
+y_hat = pretrained_model(x)
+```
+
+**Params**    
+
+| Param  | description  |
+|---|---|
+|  weights_path | Path to a pytorch checkpoint  |
+|  tags_csv | Path to meta_tags.csv file generated by the test-tube Experiment  |
+|  on_gpu | if True, puts model on GPU. Make sure to use transforms option if model devices have changed  |
+|  map_location | A dictionary mapping saved weight GPU devices to new GPU devices |
+
+**Returns**    
+
+LightningModule - The pretrained LightningModule
+
+---    
+### unfreeze 
+Unfreeze all params for inference
+```{.python}
+model = MyLightningModule(...)
+model.unfreeze()
+```
+
diff --git a/docs/Trainer/Checkpointing.md b/docs/Trainer/Checkpointing.md
index 99bf6f7e..db0d7d50 100644
--- a/docs/Trainer/Checkpointing.md
+++ b/docs/Trainer/Checkpointing.md
@@ -2,17 +2,21 @@ Lightning can automate saving and loading checkpoints.
 
 ---
 ### Model saving
-To enable checkpointing, define the checkpoint callback
+To enable checkpointing, define the checkpoint callback and give it to the trainer.
 
 ``` {.python}
 from pytorch_lightning.utils.pt_callbacks import ModelCheckpoint
 
-checkpoint = ModelCheckpoint(
+checkpoint_callback = ModelCheckpoint(
     filepath='/path/to/store/weights.ckpt',
-    save_function=None,
-    save_best_only=not hparams.keep_all_checkpoints,
+    save_best_only=True,
     verbose=True,
-    monitor=hparams.model_save_monitor_value,
-    mode=hparams.model_save_monitor_mode
+    monitor='val_loss',
+    mode='min'
 )
-```
\ No newline at end of file
+
+trainer = Trainer(checkpoint_callback=checkpoint_callback)
+```
+
+
+
diff --git a/docs/index.md b/docs/index.md
index 74a1b6b6..bae7e291 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,7 +1,7 @@
 # PYTORCH-LIGHTNING DOCUMENTATION
 
 ###### Main Docs
-- [LightningModule](Pytorch-Lightning/LightningModule)  
+- [LightningModule](LightningModule/LightningModule)  
 - [Trainer](Trainer/)  
 
 ###### New project Quick Start
diff --git a/mkdocs.yml b/mkdocs.yml
index d01098d5..585625c9 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -3,7 +3,7 @@ theme: readthedocs
 docs_dir: docs
 repo_url: https://github.com/williamFalcon/pytorch-lightning
 site_dir: 'site'
-site_description: 'Documentation for Pytorch Pytorch-Lightning, the researcher version of keras.'
+site_description: 'Documentation for Pytorch LightningModule, the researcher version of keras.'
 
 dev_addr: '0.0.0.0:8000'
 #google_analytics: ['UA-aasd', 'sitename']