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RoBERTa for RLHF Stage 2 & 3 (still in testing)

Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894d.

Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

Update test_ci.sh

Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

Add RoBERTa for RLHF Stage 2 & 3 (test)

RoBERTa for RLHF Stage 2 & 3 (still in testing)

Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894d.

Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

Update test_ci.sh

Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

update roberta with coati

chat ci update

Revert "chat ci update"

This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846.

[test]chat_update_ci

Update test_ci.sh

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test

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update test ci

update

update

update

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This commit is contained in:
Camille Zhong
2023-03-22 17:18:13 +08:00
parent d0fbd4b86f
commit 36a519b49f
4 changed files with 109 additions and 95 deletions

View File

@@ -23,7 +23,8 @@ class GPTActor(Actor):
config: Optional[GPT2Config] = None,
checkpoint: bool = False,
lora_rank: int = 0,
lora_train_bias: str = 'none') -> None:
lora_train_bias: str = 'none',
**kwargs) -> None:
if pretrained is not None:
model = GPT2LMHeadModel.from_pretrained(pretrained)
elif config is not None:
@@ -32,4 +33,4 @@ class GPTActor(Actor):
model = GPT2LMHeadModel(GPT2Config())
if checkpoint:
model.gradient_checkpointing_enable()
super().__init__(model, lora_rank, lora_train_bias)
super().__init__(model, lora_rank, lora_train_bias, **kwargs)

View File

@@ -24,7 +24,8 @@ class GPTCritic(Critic):
config: Optional[GPT2Config] = None,
checkpoint: bool = False,
lora_rank: int = 0,
lora_train_bias: str = 'none') -> None:
lora_train_bias: str = 'none',
**kwargs) -> None:
if pretrained is not None:
model = GPT2Model.from_pretrained(pretrained)
elif config is not None:
@@ -34,4 +35,4 @@ class GPTCritic(Critic):
if checkpoint:
model.gradient_checkpointing_enable()
value_head = nn.Linear(model.config.n_embd, 1)
super().__init__(model, value_head, lora_rank, lora_train_bias)
super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)