Fixed typos for Tutorials and Guides docs (#3274)

huggingface · Dec 6, 2024 · cb8b7c6 · cb8b7c6
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diff --git a/docs/source/basic_tutorials/migration.md b/docs/source/basic_tutorials/migration.md
@@ -145,7 +145,7 @@ Set the mixed precision type to use in the [`Accelerator`], and then use the [`~
 ```diff
 + accelerator = Accelerator(mixed_precision="fp16")
 + with accelerator.autocast():
-      loss = complex_loss_function(outputs, target):
+      loss = complex_loss_function(outputs, target)
 ```
 
 ## Save and load

diff --git a/docs/source/basic_tutorials/notebook.md b/docs/source/basic_tutorials/notebook.md
@@ -327,7 +327,7 @@ def training_loop(mixed_precision="fp16", seed: int = 42, batch_size: int = 64):
     # Build dataloaders
     train_dataloader, eval_dataloader = get_dataloaders(batch_size)
 
-    # Instantiate the model (you build the model here so that the seed also controls new weight initaliziations)
+    # Instantiate the model (you build the model here so that the seed also controls new weight initializations)
     model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id))
 
     # Freeze the base model

diff --git a/docs/source/usage_guides/quantization.md b/docs/source/usage_guides/quantization.md
@@ -135,4 +135,4 @@ Note that you don’t need to pass `device_map` when loading the model for train
 
 ### Example demo - running GPT2 1.5b on a Google Colab
 
-Check out the Google Colab [demo](https://colab.research.google.com/drive/1T1pOgewAWVpR9gKpaEWw4orOrzPFb3yM?usp=sharing) for running quantized models on a GTP2 model. The GPT2-1.5B model checkpoint is in FP32 which uses 6GB of memory. After quantization, it uses 1.6GB with 8-bit modules and 1.2GB with 4-bit modules.
+Check out the Google Colab [demo](https://colab.research.google.com/drive/1T1pOgewAWVpR9gKpaEWw4orOrzPFb3yM?usp=sharing) for running quantized models on a GPT2 model. The GPT2-1.5B model checkpoint is in FP32 which uses 6GB of memory. After quantization, it uses 1.6GB with 8-bit modules and 1.2GB with 4-bit modules.
diff --git a/docs/source/usage_guides/tracking.md b/docs/source/usage_guides/tracking.md
@@ -15,7 +15,7 @@ rendered properly in your Markdown viewer.
 
 # Experiment trackers
 
-There are a large number of experiment tracking API's available, however getting them all to work with in a multi-processing environment can oftentimes be complex.
+There are a large number of experiment tracking APIs available, however getting them all to work in a multi-processing environment can oftentimes be complex.
 Accelerate provides a general tracking API that can be used to log useful items during your script through [`Accelerator.log`]
 
 ## Integrated Trackers
@@ -75,7 +75,7 @@ my_model, my_optimizer, my_training_dataloader = accelerator.prepare(my_model, m
 device = accelerator.device
 my_model.to(device)
 
-for iteration in config["num_iterations"]:
+for iteration in range(config["num_iterations"]):
     for step, batch in enumerate(my_training_dataloader):
         my_optimizer.zero_grad()
         inputs, targets = batch
@@ -184,7 +184,7 @@ wandb_tracker = accelerator.get_tracker("wandb")
 From there you can interact with `wandb`'s `run` object like normal:
 
 ```python
-wandb_run.log_artifact(some_artifact_to_log)
+wandb_tracker.log_artifact(some_artifact_to_log)
 ```
 
 <Tip>
Original file line number	Diff line number	Diff line change
Expand Up		@@ -135,4 +135,4 @@ Note that you don’t need to pass `device_map` when loading the model for train

		### Example demo - running GPT2 1.5b on a Google Colab

		Check out the Google Colab [demo](https://colab.research.google.com/drive/1T1pOgewAWVpR9gKpaEWw4orOrzPFb3yM?usp=sharing) for running quantized models on a GTP2 model. The GPT2-1.5B model checkpoint is in FP32 which uses 6GB of memory. After quantization, it uses 1.6GB with 8-bit modules and 1.2GB with 4-bit modules.
		Check out the Google Colab [demo](https://colab.research.google.com/drive/1T1pOgewAWVpR9gKpaEWw4orOrzPFb3yM?usp=sharing) for running quantized models on a GPT2 model. The GPT2-1.5B model checkpoint is in FP32 which uses 6GB of memory. After quantization, it uses 1.6GB with 8-bit modules and 1.2GB with 4-bit modules.