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LLaMA-Factory-Mirror
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tiny fix

This commit is contained in:
hiyouga 2023-11-09 17:20:49 +08:00
parent 462730cbd7
commit 415bca900e
2 changed files with 4 additions and 3 deletions
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2
src/llmtuner/hparams/finetuning_args.py
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@ -45,7 +45,7 @@ class FinetuningArguments:
default=None,
metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
BLOOM & Falcon & ChatGLM choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense_h_to_4h\", \"mlp.dense_4h_to_h\"], \
BLOOM & Falcon & ChatGLM choices: [\"query_key_value\", \"dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"], \
Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
Phi-1.5 choices: [\"Wqkv\", \"out_proj\", \"fc1\", \"fc2\"], \

5
src/llmtuner/tuner/dpo/trainer.py
View File

@ -50,7 +50,7 @@ class CustomDPOTrainer(DPOTrainer):
self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
def _prepare_deepspeed(self, model: "PreTrainedModelWrapper"):
# Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
# adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
deepspeed_plugin = self.accelerator.state.deepspeed_plugin
config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
if model is not None:
@ -75,7 +75,8 @@ class CustomDPOTrainer(DPOTrainer):
# Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
if config_kwargs["zero_optimization"]["stage"] != 3:
config_kwargs["zero_optimization"]["stage"] = 0
# lazy load
# Lazy load
import deepspeed # type: ignore
model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
model.eval()