423A35C7/LLaMA-Factory
1
0
Fork 0
mirror of https://github.com/hiyouga/LLaMA-Factory.git synced 2025-08-24 23:02:49 +08:00
Code Issues Packages Projects Releases Wiki Activity

remove some unused params

Browse Source 
Former-commit-id: 04315c3d92ecc25537e45d5807cb38bc290dcb16
This commit is contained in:
ancv 2024-06-15 23:00:55 +07:00
parent 045eb155a2
commit 9d9f8c6531
3 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
src/llamafactory/data/processors/supervised.py
View File

@ -10,7 +10,7 @@ if TYPE_CHECKING:
from transformers import ProcessorMixin from transformers import ProcessorMixin
from transformers.tokenization_utils import PreTrainedTokenizer from transformers.tokenization_utils import PreTrainedTokenizer
from ...hparams import DataArguments, FinetuningArguments from ...hparams import DataArguments
from ..template import Template from ..template import Template

2
src/llamafactory/model/model_utils/packing.py
View File

@ -239,7 +239,7 @@ def configure_packing(config: "PretrainedConfig") -> None:
attn_implementation = getattr(config, "_attn_implementation", None) attn_implementation = getattr(config, "_attn_implementation", None)
if attn_implementation != "flash_attention_2": if attn_implementation != "flash_attention_2":
raise ValueError("Efficient packing only supports for flash_attention_2. Please set config `flash_attn` is fa2" + " " + attn_implementation) raise ValueError("Efficient packing only supports for flash_attention_2. Please set config `flash_attn` is fa2")
logger = get_logger(__name__) logger = get_logger(__name__)

2
src/llamafactory/train/kto/workflow.py
View File

@ -24,7 +24,7 @@ def run_kto(
): ):
tokenizer_module = load_tokenizer(model_args) tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"] tokenizer = tokenizer_module["tokenizer"]
dataset = get_dataset(model_args, data_args, training_args, finetuning_args, stage="kto", **tokenizer_module) dataset = get_dataset(model_args, data_args, training_args, stage="kto", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train) model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = KTODataCollatorWithPadding( data_collator = KTODataCollatorWithPadding(