release v0.6.2

README.md

@@ -305,7 +305,7 @@ cd LLaMA-Factory
 pip install -e .[metrics]
 ```
 
-Extra dependencies available: deepspeed, metrics, unsloth, galore, vllm, bitsandbytes, gptq, awq, aqlm, qwen, quality
+Extra dependencies available: deepspeed, metrics, unsloth, galore, vllm, bitsandbytes, gptq, awq, aqlm, qwen, modelscope, quality
 
 <details><summary>For Windows users</summary>
 

README_zh.md

@@ -305,7 +305,7 @@ cd LLaMA-Factory
 pip install -e .[metrics]
 ```
 
-可选的额外依赖项：deepspeed、metrics、unsloth、galore、vllm、bitsandbytes、gptq、awq、aqlm、qwen、quality
+可选的额外依赖项：deepspeed、metrics、unsloth、galore、vllm、bitsandbytes、gptq、awq、aqlm、qwen、modelscope、quality
 
 <details><summary>Windows 用户指南</summary>
 

setup.py

@@ -30,6 +30,7 @@ extra_require = {
     "awq": ["autoawq"],
     "aqlm": ["aqlm[gpu]>=1.1.0"],
     "qwen": ["tiktoken", "transformers_stream_generator"],
+    "modelscope": ["modelscope"],
     "quality": ["ruff"],
 }
 

src/llmtuner/__init__.py

@@ -7,5 +7,5 @@ from .train import export_model, run_exp
 from .webui import create_ui, create_web_demo
 
 
-__version__ = "0.6.2.dev0"
+__version__ = "0.6.2"
 __all__ = ["create_app", "ChatModel", "Evaluator", "export_model", "run_exp", "create_ui", "create_web_demo"]

src/llmtuner/data/loader.py

@@ -6,7 +6,7 @@ from datasets import load_dataset, load_from_disk
 
 from ..extras.constants import FILEEXT2TYPE
 from ..extras.logging import get_logger
-from ..extras.misc import is_path_available
+from ..extras.misc import has_tokenized_data
 from .aligner import align_dataset
 from .parser import get_dataset_list
 from .preprocess import get_preprocess_and_print_func
@@ -81,7 +81,9 @@ def load_single_dataset(
                 cache_dir=cache_dir,
                 token=model_args.ms_hub_token,
                 use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
-            ).to_hf_dataset()
+            )
+            if isinstance(dataset, MsDataset):
+                dataset = dataset.to_hf_dataset()
         except ImportError:
             raise ImportError("Please install modelscope via `pip install modelscope -U`")
     else:
@@ -125,7 +127,7 @@ def get_dataset(
 
     # Load tokenized dataset
     if data_args.tokenized_path is not None:
-        if not is_path_available(data_args.tokenized_path):
+        if has_tokenized_data(data_args.tokenized_path):
             logger.warning("Loading dataset from disk will ignore other data arguments.")
             dataset = load_from_disk(data_args.tokenized_path)
             logger.info("Loaded tokenized dataset from {}.".format(data_args.tokenized_path))

src/llmtuner/data/parser.py

@@ -53,12 +53,19 @@ class DatasetAttr:
 
 
 def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
-    dataset_names = [ds.strip() for ds in data_args.dataset.split(",")] if data_args.dataset is not None else []
+    if data_args.dataset is not None:
+        dataset_names = [ds.strip() for ds in data_args.dataset.split(",")]
+    else:
+        dataset_names = []
+
+    if data_args.dataset_dir == "ONLINE":
+        dataset_info = None
+    else:
         try:
             with open(os.path.join(data_args.dataset_dir, DATA_CONFIG), "r") as f:
                 dataset_info = json.load(f)
         except Exception as err:
-        if data_args.dataset is not None:
+            if len(dataset_names) != 0:
                 raise ValueError(
                     "Cannot open {} due to {}.".format(os.path.join(data_args.dataset_dir, DATA_CONFIG), str(err))
                 )
@@ -69,6 +76,12 @@ def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
 
     dataset_list: List[DatasetAttr] = []
     for name in dataset_names:
+        if dataset_info is None:
+            load_from = "ms_hub" if use_modelscope() else "hf_hub"
+            dataset_attr = DatasetAttr(load_from, dataset_name=name)
+            dataset_list.append(dataset_attr)
+            continue
+
         if name not in dataset_info:
             raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
 

src/llmtuner/extras/misc.py

@@ -193,16 +193,11 @@ def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
         return torch.float32
 
 
-def is_path_available(path: os.PathLike) -> bool:
+def has_tokenized_data(path: os.PathLike) -> bool:
     r"""
-    Checks if the path is empty or not exist.
+    Checks if the path has a tokenized dataset.
     """
-    if not os.path.exists(path):
-        return True
-    elif os.path.isdir(path) and not os.listdir(path):
-        return True
-    else:
-        return False
+    return os.path.isdir(path) and len(os.listdir(path)) > 0
 
 
 def torch_gc() -> None:

src/llmtuner/model/adapter.py

@@ -139,7 +139,6 @@ def init_adapter(
                 "lora_alpha": finetuning_args.lora_alpha,
                 "lora_dropout": finetuning_args.lora_dropout,
                 "use_rslora": finetuning_args.use_rslora,
-                "modules_to_save": finetuning_args.additional_target,
             }
 
             if model_args.use_unsloth:
@@ -151,6 +150,7 @@ def init_adapter(
                 lora_config = LoraConfig(
                     task_type=TaskType.CAUSAL_LM,
                     inference_mode=False,
+                    modules_to_save=finetuning_args.additional_target,
                     use_dora=finetuning_args.use_dora,
                     **peft_kwargs,
                 )

src/llmtuner/webui/components/eval.py

@@ -18,7 +18,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
 
     with gr.Row():
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
-        dataset = gr.Dropdown(multiselect=True, scale=4)
+        dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
         preview_elems = create_preview_box(dataset_dir, dataset)
 
     input_elems.update({dataset_dir, dataset})

src/llmtuner/webui/components/train.py

@@ -23,7 +23,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=1
         )
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=1)
-        dataset = gr.Dropdown(multiselect=True, scale=4, allow_custom_value=True)
+        dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
         preview_elems = create_preview_box(dataset_dir, dataset)
 
     input_elems.update({training_stage, dataset_dir, dataset})