wassname/alignment-handbook
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🪁 (#129)

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* Add Gemma 7B recipe

* Use Gemma template

* Make it work for dolly lol

* Enable cahce

* Clean up

* DPO to the max

* DPO, DPO, DPO

* Add openhermes

* Add custom configs

* Add kwargs

* Fix config

* Bump deps

* Move old recipes

* Add doc

* Add norte

* Renable cache

* Nuke

* Clean

* Apply suggestions from code review

Co-authored-by: Alvaro Bartolome <alvaro@argilla.io>

* Fix isort

* Update README.md

* Update config_full.yaml

---------

Co-authored-by: Alvaro Bartolome <alvaro@argilla.io>
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
This commit is contained in:
lewtun
2024-03-01 17:29:42 +01:00
committed by GitHub
parent d17fd7cd3b
commit ff618a4d13
10 changed files with 150 additions and 25 deletions
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scripts/run_dpo.py
+10 -10
View File
@@ -197,16 +197,6 @@ def main():
logger.info("*** Training complete ***")
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] = len(raw_datasets["test"])
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
##################################
# Save model and create model card
##################################
@@ -227,6 +217,16 @@ def main():
trainer.model.config.use_cache = True
trainer.model.config.save_pretrained(training_args.output_dir)
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] = len(raw_datasets["test"])
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if training_args.push_to_hub is True:
logger.info("Pushing to hub...")
trainer.push_to_hub(**kwargs)
scripts/run_sft.py
+11 -11
View File
@@ -134,7 +134,6 @@ def main():
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
logger.info("*** Model loaded! ***")
########################
# Initialize the Trainer
@@ -150,6 +149,7 @@ def main():
tokenizer=tokenizer,
packing=True,
peft_config=get_peft_config(model_args),
dataset_kwargs=training_args.dataset_kwargs,
)
###############
@@ -168,16 +168,6 @@ def main():
trainer.save_metrics("train", metrics)
trainer.save_state()
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] = len(eval_dataset)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
##################################
# Save model and create model card
##################################
@@ -198,6 +188,16 @@ def main():
trainer.model.config.use_cache = True
trainer.model.config.save_pretrained(training_args.output_dir)
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] = len(eval_dataset)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if training_args.push_to_hub is True:
logger.info("Pushing to hub...")
trainer.push_to_hub(**kwargs)