fix #1558

src/llmtuner/extras/misc.py

@@ -66,8 +66,7 @@ def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
 
 def get_current_device() -> str:
     import accelerate
-    from accelerate import Accelerator
-    dummy_accelerator = Accelerator()
+    dummy_accelerator = accelerate.Accelerator()
     if accelerate.utils.is_xpu_available():
         return "xpu:{}".format(dummy_accelerator.local_process_index)
     else:

src/llmtuner/model/__init__.py

@@ -2,4 +2,4 @@
 
 from llmtuner.model.loader import load_model_and_tokenizer
 from llmtuner.model.parser import get_train_args, get_infer_args, get_eval_args
-from llmtuner.model.utils import dispatch_model, generate_model_card, load_valuehead_params
+from llmtuner.model.utils import dispatch_model, get_modelcard_args, load_valuehead_params

src/llmtuner/model/utils.py

@@ -71,7 +71,7 @@ def find_all_linear_modules(
     return list(module_names)
 
 
-def generate_model_card(
+def get_modelcard_args(
     model_args: "ModelArguments",
     data_args: "DataArguments",
     finetuning_args: "FinetuningArguments"

src/llmtuner/train/dpo/workflow.py

@@ -1,6 +1,5 @@
 # Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py
 
-from peft import PeftModel
 from typing import TYPE_CHECKING, Optional, List
 from transformers import Seq2SeqTrainingArguments
 
@@ -8,10 +7,10 @@ from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.hparams import ModelArguments
-from llmtuner.model import generate_model_card, load_model_and_tokenizer
-from llmtuner.train.utils import create_ref_model
+from llmtuner.model import load_model_and_tokenizer
 from llmtuner.train.dpo.collator import DPODataCollatorWithPadding
 from llmtuner.train.dpo.trainer import CustomDPOTrainer
+from llmtuner.train.utils import create_modelcard_and_push, create_ref_model
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
@@ -78,8 +77,4 @@ def run_dpo(
         trainer.save_metrics("eval", metrics)
 
     # Create model card
-    if training_args.do_train:
-        if training_args.push_to_hub:
-            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
-        else:
-            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

src/llmtuner/train/pt/workflow.py

@@ -6,7 +6,8 @@ from transformers import DataCollatorForLanguageModeling, Trainer
 
 from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import load_model_and_tokenizer
+from llmtuner.train.utils import create_modelcard_and_push
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
@@ -58,8 +59,4 @@ def run_pt(
         trainer.save_metrics("eval", metrics)
 
     # Create model card
-    if training_args.do_train:
-        if training_args.push_to_hub:
-            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
-        else:
-            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

src/llmtuner/train/rm/workflow.py

@@ -6,10 +6,11 @@ from transformers import Seq2SeqTrainingArguments
 from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.callbacks import SavePeftModelCallback
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import load_model_and_tokenizer
 from llmtuner.train.rm.collator import PairwiseDataCollatorWithPadding
 from llmtuner.train.rm.metric import compute_accuracy
 from llmtuner.train.rm.trainer import PairwiseTrainer
+from llmtuner.train.utils import create_modelcard_and_push
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
@@ -68,8 +69,4 @@ def run_rm(
         trainer.save_predictions(predict_results)
 
     # Create model card
-    if training_args.do_train:
-        if training_args.push_to_hub:
-            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
-        else:
-            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

src/llmtuner/train/sft/workflow.py

@@ -7,9 +7,10 @@ from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.misc import get_logits_processor
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.model import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import load_model_and_tokenizer
 from llmtuner.train.sft.metric import ComputeMetrics
 from llmtuner.train.sft.trainer import CustomSeq2SeqTrainer
+from llmtuner.train.utils import create_modelcard_and_push
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
@@ -90,8 +91,4 @@ def run_sft(
         trainer.save_predictions(predict_results)
 
     # Create model card
-    if training_args.do_train:
-        if training_args.push_to_hub:
-            trainer.push_to_hub(**generate_model_card(model_args, data_args, finetuning_args))
-        else:
-            trainer.create_model_card(**generate_model_card(model_args, data_args, finetuning_args))
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

src/llmtuner/train/utils.py

@@ -3,16 +3,35 @@ from typing import TYPE_CHECKING, Literal, Union
 
 from llmtuner.extras.logging import get_logger
 from llmtuner.hparams import ModelArguments, FinetuningArguments
-from llmtuner.model import load_model_and_tokenizer, load_valuehead_params
+from llmtuner.model import get_modelcard_args, load_model_and_tokenizer, load_valuehead_params
 
 if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, Trainer
     from transformers.modeling_utils import PreTrainedModel
     from trl import AutoModelForCausalLMWithValueHead
+    from llmtuner.hparams import DataArguments
 
 
 logger = get_logger(__name__)
 
 
+def create_modelcard_and_push(
+    trainer: "Trainer",
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments"
+) -> None:
+    if training_args.do_train:
+        if training_args.push_to_hub:
+            trainer.push_to_hub(**get_modelcard_args(model_args, data_args, finetuning_args))
+            return
+        try:
+            trainer.create_model_card(**get_modelcard_args(model_args, data_args, finetuning_args))
+        except Exception as err:
+            logger.warning("Failed to create model card: {}".format(str(err)))
+
+
 def create_ref_model(
     model_args: "ModelArguments",
     finetuning_args: "FinetuningArguments",