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[CI] fix dpo gpu ci tests (#2189)
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* fix dpo ci test

* color-blind
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kashif authored Oct 7, 2024
1 parent 7005ab4 commit 51ca76b
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Showing 2 changed files with 8 additions and 8 deletions.
4 changes: 2 additions & 2 deletions scripts/log_example_reports.py
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ def main(text_file_name, slack_channel_name=None):
"type": "section",
"text": {
"type": "plain_text",
"text": "🔴 Something is wrong with the workflow please check ASAP!"
"text": " Something is wrong with the workflow please check ASAP!"
"Something went wrong there is no text file being produced. Please check ASAP.",
"emoji": True,
},
Expand All @@ -82,7 +82,7 @@ def main(text_file_name, slack_channel_name=None):

for test_name, failed in final_results.items():
failed_table = tabulate(
[[test_name, "🟢" if not failed else "🔴"]],
[[test_name, "" if not failed else ""]],
headers=["Test Name", "Status"],
showindex="always",
tablefmt="grid",
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12 changes: 6 additions & 6 deletions tests/slow/test_dpo_slow.py
Original file line number Diff line number Diff line change
Expand Up @@ -85,9 +85,9 @@ def test_dpo_bare_model(self, model_id, loss_type, pre_compute_logits):
model=model,
ref_model=None,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=tokenizer,
train_dataset=self.dataset,
eval_dataset=self.dataset,
)

# train the model
Expand Down Expand Up @@ -142,9 +142,9 @@ def test_dpo_peft_model(self, model_id, loss_type, pre_compute_logits, gradient_
model=model,
ref_model=None,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=tokenizer,
train_dataset=self.dataset,
eval_dataset=self.dataset,
peft_config=self.peft_config,
)

Expand Down Expand Up @@ -206,9 +206,9 @@ def test_dpo_peft_model_qlora(self, model_id, loss_type, pre_compute_logits, gra
model=model,
ref_model=None,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=tokenizer,
train_dataset=self.dataset,
eval_dataset=self.dataset,
peft_config=self.peft_config,
)

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