support llama pro #2338 , add rslora

src/llmtuner/model/utils.py

@@ -1,5 +1,5 @@
 import inspect
-from typing import TYPE_CHECKING, Any, Dict, List
+from typing import TYPE_CHECKING, Dict, List
 
 import torch
 from transformers import PreTrainedModel
@@ -13,7 +13,7 @@ from ..extras.misc import get_current_device
 if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedTokenizer
 
-    from ..hparams import DataArguments, FinetuningArguments, ModelArguments
+    from ..hparams import ModelArguments
 
 
 logger = get_logger(__name__)
@@ -76,18 +76,6 @@ def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
     return list(module_names)
 
 
-def get_modelcard_args(
-    model_args: "ModelArguments", data_args: "DataArguments", finetuning_args: "FinetuningArguments"
-) -> Dict[str, Any]:
-    return {
-        "tasks": "text-generation",
-        "license": "other",
-        "finetuned_from": model_args.model_name_or_path,
-        "dataset": [dataset.strip() for dataset in data_args.dataset.split(",")],
-        "tags": ["llama-factory"] + (["lora"] if finetuning_args.finetuning_type == "lora" else []),
-    }
-
-
 def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
     r"""
     Loads value head parameters from Hugging Face Hub or local disk.