[docs] refine the doc for train with a script (#33423)

* add xpu note * add one more case * add more * Update docs/source/en/run_scripts.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
huggingface · Sep 12, 2024 · a05ce55 · a05ce55
1 parent 5c6257d
commit a05ce55
Show file tree

Hide file tree

Showing 3 changed files with 6 additions and 6 deletions.
diff --git a/docs/source/en/run_scripts.md b/docs/source/en/run_scripts.md
@@ -126,7 +126,7 @@ python examples/tensorflow/summarization/run_summarization.py  \
 
 The [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) supports distributed training and mixed precision, which means you can also use it in a script. To enable both of these features:
 
-- Add the `fp16` argument to enable mixed precision.
+- Add the `fp16` or `bf16` argument to enable mixed precision. XPU devices only supports `bf16` for mixed precision training.
 - Set the number of GPUs to use with the `nproc_per_node` argument.
 
 ```bash
@@ -287,7 +287,7 @@ Another helpful option to enable is resuming training from a previous checkpoint
 The first method uses the `output_dir previous_output_dir` argument to resume training from the latest checkpoint stored in `output_dir`. In this case, you should remove `overwrite_output_dir`:
 
 ```bash
-python examples/pytorch/summarization/run_summarization.py
+python examples/pytorch/summarization/run_summarization.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
@@ -304,7 +304,7 @@ python examples/pytorch/summarization/run_summarization.py
 The second method uses the `resume_from_checkpoint path_to_specific_checkpoint` argument to resume training from a specific checkpoint folder.
 
 ```bash
-python examples/pytorch/summarization/run_summarization.py
+python examples/pytorch/summarization/run_summarization.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \
@@ -334,7 +334,7 @@ To give your repository a specific name, use the `push_to_hub_model_id` argument
 The following example shows how to upload a model with a specific repository name:
 
 ```bash
-python examples/pytorch/summarization/run_summarization.py
+python examples/pytorch/summarization/run_summarization.py \
     --model_name_or_path google-t5/t5-small \
     --do_train \
     --do_eval \

diff --git a/docs/source/en/tasks/summarization.md b/docs/source/en/tasks/summarization.md
@@ -205,7 +205,7 @@ At this point, only three steps remain:
 ...     save_total_limit=3,
 ...     num_train_epochs=4,
 ...     predict_with_generate=True,
-...     fp16=True,
+...     fp16=True, #change to bf16=True for XPU
 ...     push_to_hub=True,
 ... )
 

diff --git a/docs/source/en/tasks/translation.md b/docs/source/en/tasks/translation.md
@@ -212,7 +212,7 @@ At this point, only three steps remain:
 ...     save_total_limit=3,
 ...     num_train_epochs=2,
 ...     predict_with_generate=True,
-...     fp16=True,
+...     fp16=True, #change to bf16=True for XPU
 ...     push_to_hub=True,
 ... )