Updating Docs and adding new examples

README.md

@@ -192,7 +192,7 @@ print(f"Hey ! , here's where your model saved {output.checkpoint_path}")
 ```
 
 > [!NOTE]
-> You Can use Lora too, both for DPO and SFT Trainers.
+> You Can use Lora too, for DPO, ORPO and SFT Trainers.
 
 ## FineTuning
 

docs/generated-cli-cli.md

@@ -0,0 +1,2 @@
+# cli.cli
+::: src.python.easydel.cli.cli

generate_documentations.py

@@ -121,11 +121,11 @@ def main():
       handlers:
         python:
           options:
-            docstring_style: sphinx
+            docstring_style: google
 
 repo_url: https://github.com/erfanzar/EasyDeL
 site_author: Erfan Zare Chavoshi
-site_name: easydel
+site_name: EasyDeL
 copyright: Erfan Zare Chavoshi-easydel
 
 theme:
@@ -138,9 +138,9 @@ def main():
 
     statics = {
         ("Home",): "index.md",
-        ("install",): "Install.md",
-        ("AvailableModels",): "AvailableModels.md",
-        ("EasyBIT",): "Bits.md",
+        ("Install",): "Install.md",
+        ("Available models",): "AvailableModels.md",
+        ("Easy Bits",): "Bits.md",
         ("Examples", "EasyState"): "EasyStateExample.md",
         ("Examples", "LoRA and Transfer Learning"): "LoRA-TransferLearningExample.md",
         ("Examples", "Fine Tuning Example"): "FineTuningExample.md",
@@ -154,7 +154,7 @@ def main():
         ("Examples", "MosaicMPT Models"): "MosaicMPT.md",
         ("Examples", "Easy Attention"): "AttentionModuleExample.md",
         ("Examples", "Model Parameter Quantization"): "Parameter-Quantization.md",
-        ("CONTRIBUTING",): "CONTRIBUTING.md"
+        ("Contributing",): "CONTRIBUTING.md"
 
     }
     cache = {("APIs",) + k: v for k, v in cache.items()}

mkdocs.yml

@@ -1,5 +1,7 @@
 nav:
   - APIs:
+    - Cli:
+      - Cli: generated-cli-cli.md
     - Data Preprocessing:
       - Processor: generated-data_preprocessing-_processor.md
     - Etils:
@@ -188,9 +190,9 @@ nav:
       - Prompters: generated-utils-prompters.md
       - Tensor Utils: generated-utils-tensor_utils.md
       - Utils: generated-utils-utils.md
-  - AvailableModels: AvailableModels.md
-  - CONTRIBUTING: CONTRIBUTING.md
-  - EasyBIT: Bits.md
+  - Available models: AvailableModels.md
+  - Contributing: CONTRIBUTING.md
+  - Easy Bits: Bits.md
   - Examples:
     - DataProcessing: DataProcessing.md
     - Easy Attention: AttentionModuleExample.md
@@ -206,19 +208,19 @@ nav:
     - MosaicMPT Models: MosaicMPT.md
     - PytorchServer: PyTorchServer.md
   - Home: index.md
-  - install: Install.md
+  - Install: Install.md
 
 plugins:
   - search
   - mkdocstrings:
       handlers:
         python:
           options:
-            docstring_style: sphinx
+            docstring_style: google
 
 repo_url: https://github.com/erfanzar/EasyDeL
 site_author: Erfan Zare Chavoshi
-site_name: easydel
+site_name: EasyDeL
 copyright: Erfan Zare Chavoshi-easydel
 
 theme:

src/python/easydel/etils/auto_tx.py

@@ -20,20 +20,26 @@ def get_optimizer_and_scheduler(
         weight_decay: float = 0.02,
         warmup_steps: int = 0
 ):
-    """
-    The get_optimizer_and_scheduler function is a helper function that returns an optimizer and scheduler
+    """The get_optimizer_and_scheduler function is a helper function that returns an optimizer and scheduler
         based on the parameters passed to it.
 
-    :param optimizer: AVAILABLE_OPTIMIZERS: Choose the optimizer
-    :param scheduler: AVAILABLE_SCHEDULERS: Determine the learning rate scheduler
-    :param steps: int: Specify the number of steps in the training process
-    :param learning_rate: float: Set the learning rate for the optimizer
-    :param learning_rate_end: float: Set the final learning rate
-    :param gradient_accumulation_steps: int: Accumulate the gradients before updating the weights
-    :param extra_optimizer_kwargs: dict | None: Pass extra arguments to the optimizer
-    :param weight_decay: float: Set the weight decay for adamw optimizer
-    :param warmup_steps: int: Specify the number of steps to warm up the learning rate
-    :return: A tuple of two objects: (Optimizer and scheduler)
+    Args:
+        optimizer: AVAILABLE_OPTIMIZERS: Choose the optimizer
+        scheduler: AVAILABLE_SCHEDULERS: Determine the learning rate
+            scheduler
+        steps: int: Specify the number of steps in the training process
+        learning_rate: float: Set the learning rate for the optimizer
+        learning_rate_end: float: Set the final learning rate
+        gradient_accumulation_steps: int: Accumulate the gradients
+            before updating the weights
+        extra_optimizer_kwargs: dict | None: Pass extra arguments to the
+            optimizer
+        weight_decay: float: Set the weight decay for adamw optimizer
+        warmup_steps: int: Specify the number of steps to warm up the
+            learning rate
+
+    Returns:
+        A tuple of two objects: (Optimizer and scheduler)
     """
     if extra_optimizer_kwargs is None:
         extra_optimizer_kwargs = {}

src/python/easydel/etils/configs.py

@@ -396,13 +396,14 @@
 
 
 def get_config(model_type: str, struct: str):
-    """
-    The get_config function takes in a model_type and struct, and returns the corresponding config.
+    """The get_config function takes in a model_type and struct, and returns the corresponding config.
+
+    Args:
+        model_type: str: Determine which model to use
+        struct: str: Specify the structure of the model
 
-    :param model_type: str: Determine which model to use
-    :param struct: str: Specify the structure of the model
-    :return: A dictionary of hyperparameters
-    
+    Returns:
+        A dictionary of hyperparameters
     """
     if model_type == "llama":
         return llama_configs[struct]