ros/LLaMA-Factory
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Former-commit-id: 7cfede95df22a9ff236788f04159b6b16b8d04bb
This commit is contained in:
hiyouga
2024-10-29 09:07:46 +00:00
parent 8f5921692e
commit 248d5daaff
66 changed files with 1028 additions and 1044 deletions
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12
src/llamafactory/train/trainer_utils.py
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@@ -116,7 +116,7 @@ def create_ref_model(
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
logger.info("Created reference model from {}".format(finetuning_args.ref_model))
logger.info(f"Created reference model from {finetuning_args.ref_model}")
else:
if finetuning_args.finetuning_type == "lora":
ref_model = None
@@ -140,7 +140,7 @@ def create_reward_model(
"""
if finetuning_args.reward_model_type == "api":
assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
logger.info("Use reward server {}".format(finetuning_args.reward_model))
logger.info(f"Use reward server {finetuning_args.reward_model}")
return finetuning_args.reward_model
elif finetuning_args.reward_model_type == "lora":
model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
@@ -157,7 +157,7 @@ def create_reward_model(
model.register_buffer(
"default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
)
logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
logger.info(f"Loaded adapter weights of reward model from {finetuning_args.reward_model}")
return None
else:
reward_model_args = ModelArguments.copyfrom(
@@ -171,7 +171,7 @@ def create_reward_model(
reward_model = load_model(
tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
)
logger.info("Loaded full weights of reward model from {}".format(finetuning_args.reward_model))
logger.info(f"Loaded full weights of reward model from {finetuning_args.reward_model}")
logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
return reward_model
@@ -231,7 +231,7 @@ def _create_galore_optimizer(
elif training_args.optim == "adafactor":
optim_class = GaLoreAdafactor
else:
raise NotImplementedError("Unknow optim: {}".format(training_args.optim))
raise NotImplementedError(f"Unknow optim: {training_args.optim}")
if finetuning_args.galore_layerwise:
if training_args.gradient_accumulation_steps != 1:
@@ -305,7 +305,7 @@ def _create_loraplus_optimizer(
dict(params=param_dict["embedding"], lr=embedding_lr, weight_decay=training_args.weight_decay),
]
optimizer = optim_class(param_groups, **optim_kwargs)
logger.info("Using LoRA+ optimizer with loraplus lr ratio {:.2f}.".format(finetuning_args.loraplus_lr_ratio))
logger.info(f"Using LoRA+ optimizer with loraplus lr ratio {finetuning_args.loraplus_lr_ratio:.2f}.")
return optimizer