添加子目录saves到llama-factory-main本地文件夹

时间: 2025-06-27 21:11:37 浏览: 9
<think>好的,我需要帮助用户在本地文件夹 llama-factory-main 中添加一个名为 saves 的子目录。用户提到了使用 llamafactory-cli 命令,这可能涉及命令行操作。首先,用户可能使用的是Windows系统,或者也可能是macOS/Linux,但根据常见情况,我先假设是Windows。 用户想要在 llama-factory-main 文件夹下创建 saves 子目录。创建目录通常可以通过命令行或者文件资源管理器手动完成。使用命令行的话,mkdir 是常用的命令。需要确认用户当前的工作目录是否已经是 llama-factory-main,或者需要先导航到该目录。 如果用户已经在目标目录下,直接运行 mkdir saves 即可。但可能需要使用绝对路径或者相对路径。例如,如果当前目录不是 llama-factory-main,可能需要先执行 cd 命令切换目录。另外,用户提到的引用[^1]中的命令是 llamafactory-cli export 的示例,可能该工具本身是否有创建目录的功能?不过通常创建目录不需要特定工具,用系统命令更直接。 另外,用户可能希望用Python脚本创建目录,这可以通过os模块的mkdir或makedirs函数实现。但用户的问题更偏向于直接在系统中操作,所以命令行方法更直接。需要提醒用户注意权限问题,确保有权限在目标目录下创建文件夹。总结可能的步骤:1. 打开终端或命令提示符;2. 导航到 llama-factory-main 目录;3. 执行 mkdir saves。如果是跨平台,命令可能略有不同,比如在Linux/macOS中同样使用mkdir,但路径分隔符是正斜杠。此外,如果saves目录已经存在,mkdir会报错,可能需要检查是否存在或添加-p参数(在Unix系统中),但Windows的mkdir需要加参数/d或者直接报错。需要根据用户的实际系统给予相应建议。</think>### 分步说明 1. **打开命令行工具**: - Windows: 使用 `Win + R` 打开运行窗口,输入 `cmd` 回车 - macOS/Linux: 打开终端(Terminal) 2. **定位到目标文件夹**: ```bash cd /path/to/llama-factory-main ``` > 将 `/path/to` 替换为实际路径,例如 Windows 可能是 `D:\projects\llama-factory-main` 3. **创建子目录**: ```bash mkdir saves ``` 4. **验证结果**: ```bash dir # Windows ls # macOS/Linux ``` 应该能看到新建的 `saves` 目录
阅读全文

相关推荐

/home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] ***************************************** W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] ***************************************** /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources [rank0]: Traceback (most recent call last): [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank0]: return importlib.import_module("." + module_name, self.__name__) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank0]: return _bootstrap._gcd_import(name[level:], package, level) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank0]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank0]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank0]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank0]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank0]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank0]: from .tokenization_llama import LlamaTokenizer [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank0]: import sentencepiece as spm [rank0]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank0]: from . import _sentencepiece [rank0]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank0]: The above exception was the direct cause of the following exception: [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank0]: tokenizer = AutoTokenizer.from_pretrained( [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank0]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank0]: return getattr(module, class_name) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank0]: module = self._get_module(self._class_to_module[name]) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank0]: raise RuntimeError( [rank0]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank0]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank0]: The above exception was the direct cause of the following exception: [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank0]: launch() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank0]: run_exp() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank0]: _training_function(config={"args": args, "callbacks": callbacks}) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank0]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank0]: tokenizer_module = load_tokenizer(model_args) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank0]: raise OSError("Failed to load tokenizer.") from e [rank0]: OSError: Failed to load tokenizer. [rank3]: Traceback (most recent call last): [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank3]: return importlib.import_module("." + module_name, self.__name__) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank3]: return _bootstrap._gcd_import(name[level:], package, level) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank3]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank3]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank3]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank3]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank3]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank3]: from .tokenization_llama import LlamaTokenizer [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank3]: import sentencepiece as spm [rank3]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank3]: from . import _sentencepiece [rank3]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank3]: The above exception was the direct cause of the following exception: [rank3]: Traceback (most recent call last): [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank3]: tokenizer = AutoTokenizer.from_pretrained( [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank3]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank3]: return getattr(module, class_name) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank3]: module = self._get_module(self._class_to_module[name]) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank3]: raise RuntimeError( [rank3]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank3]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank3]: The above exception was the direct cause of the following exception: [rank3]: Traceback (most recent call last): [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank3]: launch() [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank3]: run_exp() [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank3]: _training_function(config={"args": args, "callbacks": callbacks}) [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank3]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank3]: tokenizer_module = load_tokenizer(model_args) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank3]: raise OSError("Failed to load tokenizer.") from e [rank3]: OSError: Failed to load tokenizer. [rank1]: Traceback (most recent call last): [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank1]: return importlib.import_module("." + module_name, self.__name__) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank1]: return _bootstrap._gcd_import(name[level:], package, level) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank1]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank1]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank1]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank1]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank1]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank1]: from .tokenization_llama import LlamaTokenizer [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank1]: import sentencepiece as spm [rank1]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank1]: from . import _sentencepiece [rank1]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank1]: The above exception was the direct cause of the following exception: [rank1]: Traceback (most recent call last): [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank1]: tokenizer = AutoTokenizer.from_pretrained( [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank1]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank1]: return getattr(module, class_name) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank1]: module = self._get_module(self._class_to_module[name]) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank1]: raise RuntimeError( [rank1]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank1]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank1]: The above exception was the direct cause of the following exception: [rank1]: Traceback (most recent call last): [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank1]: launch() [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank1]: run_exp() [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank1]: _training_function(config={"args": args, "callbacks": callbacks}) [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank1]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank1]: tokenizer_module = load_tokenizer(model_args) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank1]: raise OSError("Failed to load tokenizer.") from e [rank1]: OSError: Failed to load tokenizer. [rank2]: Traceback (most recent call last): [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank2]: return importlib.import_module("." + module_name, self.__name__) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank2]: return _bootstrap._gcd_import(name[level:], package, level) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank2]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank2]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank2]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank2]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank2]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank2]: from .tokenization_llama import LlamaTokenizer [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank2]: import sentencepiece as spm [rank2]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank2]: from . import _sentencepiece [rank2]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank2]: The above exception was the direct cause of the following exception: [rank2]: Traceback (most recent call last): [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank2]: tokenizer = AutoTokenizer.from_pretrained( [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank2]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank2]: return getattr(module, class_name) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank2]: module = self._get_module(self._class_to_module[name]) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank2]: raise RuntimeError( [rank2]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank2]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank2]: The above exception was the direct cause of the following exception: [rank2]: Traceback (most recent call last): [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank2]: launch() [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank2]: run_exp() [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank2]: _training_function(config={"args": args, "callbacks": callbacks}) [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank2]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank2]: tokenizer_module = load_tokenizer(model_args) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank2]: raise OSError("Failed to load tokenizer.") from e [rank2]: OSError: Failed to load tokenizer. [rank0]:[W703 16:13:30.861219244 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) W0703 16:13:31.449512 3913223 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3913282 closing signal SIGTERM W0703 16:13:31.450263 3913223 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3913283 closing signal SIGTERM W0703 16:13:31.450724 3913223 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3913284 closing signal SIGTERM E0703 16:13:31.765744 3913223 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 3913281) of binary: /usr/bin/python3.11 Traceback (most recent call last): File "/usr/local/bin/torchrun", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 892, in main run(args) File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 883, in run elastic_launch( File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 270, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2025-07-03_16:13:31 host : wiseatc-Super-Server rank : 0 (local_rank: 0) exitcode : 1 (pid: 3913281) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ Traceback (most recent call last): File "/home/wiseatc/.local/bin/llamafactory-cli", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/wiseatc/LLaMA-Factory/src/llamafactory/cli.py", line 130, in main process = subprocess.run( ^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/subprocess.py", line 569, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['torchrun', '--nnodes', '1', '--node_rank', '0', '--nproc_per_node', '4', '--master_addr', '127.0.0.1', '--master_port', '38589', '/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py', 'saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-07-03-16-00-01/training_args.yaml']' returned non-zero exit status 1.

INFO|2025-03-04 21:58:56] llamafactory.model.model_utils.misc:157 >> Found linear modules: up_proj,o_proj,down_proj,v_proj,q_proj,k_proj,gate_proj [INFO|2025-03-04 21:58:56] llamafactory.model.loader:157 >> trainable params: 36,929,536 || all params: 1,814,017,536 || trainable%: 2.0358 [INFO|trainer.py:746] 2025-03-04 21:58:57,032 >> Using auto half precision backend [WARNING|trainer.py:781] 2025-03-04 21:58:57,033 >> No label_names provided for model class PeftModelForCausalLM. Since PeftModel hides base models input arguments, if label_names is not given, label_names can't be set automatically within Trainer. Note that empty label_names list will be used instead. [INFO|trainer.py:2789] 2025-03-04 21:58:57,034 >> Loading model from saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-03-04-21-49-43/checkpoint-30. Traceback (most recent call last): File "/root/miniconda3/bin/llamafactory-cli", line 8, in <module> sys.exit(main()) ^^^^^^ File "/root/autodl-tmp/ai/LLaMA-Factory/src/llamafactory/cli.py", line 112, in main run_exp() File "/root/autodl-tmp/ai/LLaMA-Factory/src/llamafactory/train/tuner.py", line 93, in run_exp _training_function(config={"args": args, "callbacks": callbacks}) File "/root/autodl-tmp/ai/LLaMA-Factory/src/llamafactory/train/tuner.py", line 67, in _training_function run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) File "/root/autodl-tmp/ai/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 102, in run_sft train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/miniconda3/lib/python3.12/site-packages/transformers/trainer.py", line 2213, in train self._load_from_checkpoint(resume_from_checkpoint) File "/root/miniconda3/lib/python3.12/site-packages/transformers/trainer.py", line 2877, in _load_from_checkpoint model.load_adapter(resume_from_checkpoint, active_adapter, is_trainable=True) File "/root/miniconda3/lib/python3.12/site-packages/peft/peft_model.py", line 1117, in load_adapter load_result = set_peft_model_state_dict( ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/miniconda3/lib/python3.12/site-packages/peft/utils/save_and_load.py", line 395, in set_peft_model_state_dict load_result = model.load_state_dict(peft_model_state_dict, strict=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 2581, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for PeftModelForCausalLM: size mismatch for base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([8, 1536]) from checkpoint, the shape in current model is torch.Size([32, 1536]).

/home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] ***************************************** W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] ***************************************** /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,321 >> loading file tokenizer.model [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file tokenizer.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file tokenizer_config.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file chat_template.jinja /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources [INFO|tokenization_utils_base.py:2313] 2025-07-03 16:30:43,904 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO|configuration_utils.py:697] 2025-07-03 16:30:43,913 >> loading configuration file /mnt/data1/models/1.5B/config.json [INFO|configuration_utils.py:771] 2025-07-03 16:30:43,919 >> Model config Qwen2Config { "_name_or_path": "/mnt/data1/models/1.5B", "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151643, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 131072, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 10000, "sliding_window": 4096, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_mrope": false, "use_sliding_window": false, "vocab_size": 151936 } [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file tokenizer.model [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file tokenizer.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file tokenizer_config.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file chat_template.jinja [INFO|tokenization_utils_base.py:2313] 2025-07-03 16:30:44,493 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank1]:[W703 16:30:45.102845887 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank2]:[W703 16:30:45.126706430 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank3]:[W703 16:30:45.136836682 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 120 examples [00:00, 6525.39 examples/s] Converting format of dataset (num_proc=16): 0%| | 0/120 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 0%| | 0/120 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 0%| | 0/120 [00:00<?, ? examples/s] /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank0]:[W703 16:31:05.679961201 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. [rank0]: multiprocess.pool.RemoteTraceback: [rank0]: """ [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker [rank0]: result = (True, func(*args, **kwds)) [rank0]: ^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 688, in _write_generator_to_queue [rank0]: for i, result in enumerate(func(**kwargs)): [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3501, in _map_single [rank0]: for i, example in iter_outputs(shard_iterable): [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3475, in iter_outputs [rank0]: yield i, apply_function(example, i, offset=offset) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3398, in apply_function [rank0]: processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/converter.py", line 94, in __call__ [rank0]: if self.dataset_attr.prompt and example[self.dataset_attr.prompt]: [rank0]: ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 278, in __getitem__ [rank0]: value = self.data[key] [rank0]: ~~~~~~~~~^^^^^ [rank0]: KeyError: 'instruction' [rank0]: """ [rank0]: The above exception was the direct cause of the following exception: [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank0]: launch() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank0]: run_exp() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank0]: _training_function(config={"args": args, "callbacks": callbacks}) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank0]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51, in run_sft [rank0]: dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/loader.py", line 304, in get_dataset [rank0]: dataset = _get_merged_dataset(data_args.dataset, model_args, data_args, training_args, stage) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/loader.py", line 182, in _get_merged_dataset [rank0]: datasets[dataset_name] = _load_single_dataset(dataset_attr, model_args, data_args, training_args) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/loader.py", line 162, in _load_single_dataset [rank0]: return align_dataset(dataset, dataset_attr, data_args, training_args) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/converter.py", line 279, in align_dataset [rank0]: return dataset.map( [rank0]: ^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 557, in wrapper [rank0]: out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3171, in map [rank0]: for rank, done, content in iflatmap_unordered( [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 728, in iflatmap_unordered [rank0]: [async_result.get(timeout=0.05) for async_result in async_results] [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 728, in [rank0]: [async_result.get(timeout=0.05) for async_result in async_results] [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get [rank0]: raise self._value [rank0]: KeyError: 'instruction' [rank0]:[W703 16:31:06.912491219 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) W0703 16:31:07.960560 3914856 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3914916 closing signal SIGTERM W0703 16:31:07.961188 3914856 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3914917 closing signal SIGTERM W0703 16:31:07.961536 3914856 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3914918 closing signal SIGTERM E0703 16:31:08.371267 3914856 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 3914915) of binary: /usr/bin/python3.11 Traceback (most recent call last): File "/usr/local/bin/torchrun", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 892, in main run(args) File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 883, in run elastic_launch( File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 270, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2025-07-03_16:31:07 host : wiseatc-Super-Server rank : 0 (local_rank: 0) exitcode : 1 (pid: 3914915) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ Traceback (most recent call last): File "/home/wiseatc/.local/bin/llamafactory-cli", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/wiseatc/LLaMA-Factory/src/llamafactory/cli.py", line 130, in main process = subprocess.run( ^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/subprocess.py", line 569, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['torchrun', '--nnodes', '1', '--node_rank', '0', '--nproc_per_node', '4', '--master_addr', '127.0.0.1', '--master_port', '41919', '/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py', 'saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-07-03-16-29-46/training_args.yaml']' returned non-zero exit status 1.

[INFO|2025-03-04 15:01:37] configuration_utils.py:771 >> Model config LlamaConfig { "architectures": [ "LlamaForCausalLM" ], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128000, "eos_token_id": 128009, "head_dim": 128, "hidden_act": "silu", "hidden_size": 4096, "initializer_range": 0.02, "intermediate_size": 14336, "max_position_embeddings": 8192, "mlp_bias": false, "model_type": "llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 8, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 500000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "vocab_size": 128256 } [INFO|2025-03-04 15:01:37] tokenization_utils_base.py:2500 >> tokenizer config file saved in saves/Llama-3-8B-Instruct/lora/train_2025-03-04-14-57-37/tokenizer_config.json [INFO|2025-03-04 15:01:37] tokenization_utils_base.py:2509 >> Special tokens file saved in saves/Llama-3-8B-Instruct/lora/train_2025-03-04-14-57-37/special_tokens_map.json [WARNING|2025-03-04 15:01:37] logging.py:162 >> No metric loss to plot. [WARNING|2025-03-04 15:01:37] logging.py:162 >> No metric eval_loss to plot. [WARNING|2025-03-04 15:01:37] logging.py:162 >> No metric eval_accuracy to plot. [INFO|2025-03-04 15:01:37] trainer.py:4258 >> ***** Running Evaluation ***** [INFO|2025-03-04 15:01:37] trainer.py:4260 >> Num examples = 8 [INFO|2025-03-04 15:01:37] trainer.py:4263 >> Batch size = 2 [INFO|2025-03-04 15:01:38] modelcard.py:449 >> Dropping the following result as it does not have all the necessary fields: {'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}

最新推荐

recommend-type

IP网络基础知识及原理.ppt

IP网络基础知识及原理.ppt
recommend-type

Evc Sql CE 程序开发实践与样例代码分享

在详细解释标题、描述和标签中提及的知识点之前,需要指出“压缩包子文件的文件名称列表”中的“8”可能是不完整的上下文信息。由于缺乏具体的文件列表内容,我们将主要集中在如何理解“Evc Sql CE 程序样例代码”这一主题。 标题“Evc Sql CE 程序样例代码”直接指向一个程序开发样例代码,其中“Evc”可能是某种环境或工具的缩写,但由于没有更多的上下文信息,很难精确地解释这个缩写指的是什么。不过,“Sql CE”则明确地指向了“SQL Server Compact Edition”,它是微软推出的一个轻量级数据库引擎,专为嵌入式设备和小型应用程序设计。 ### SQL Server Compact Edition (SQL CE) SQL Server Compact Edition(简称SQL CE)是微软公司提供的一个嵌入式数据库解决方案,它支持多种平台和编程语言。SQL CE适合用于资源受限的环境,如小型应用程序、移动设备以及不需要完整数据库服务器功能的场合。 SQL CE具备如下特点: - **轻量级**: 轻便易用,对系统资源占用较小。 - **易于部署**: 可以轻松地将数据库文件嵌入到应用程序中,无需单独安装。 - **支持多平台**: 能够在多种操作系统上运行,包括Windows、Windows CE和Windows Mobile等。 - **兼容性**: 支持标准的SQL语法,并且在一定程度上与SQL Server数据库系统兼容。 - **编程接口**: 提供了丰富的API供开发者进行数据库操作,支持.NET Framework和本机代码。 ### 样例代码的知识点 “Evc Sql CE 程序样例代码”这部分信息表明,存在一些示例代码,这些代码可以指导开发者如何使用SQL CE进行数据库操作。样例代码一般会涵盖以下几个方面: 1. **数据库连接**: 如何创建和管理到SQL CE数据库的连接。 2. **数据操作**: 包括数据的增删改查(CRUD)操作,这些是数据库操作中最基本的元素。 3. **事务处理**: 如何在SQL CE中使用事务,保证数据的一致性和完整性。 4. **数据表操作**: 如何创建、删除数据表,以及修改表结构。 5. **数据查询**: 利用SQL语句查询数据,包括使用 SELECT、JOIN等语句。 6. **数据同步**: 如果涉及到移动应用场景,可能需要了解如何与远程服务器进行数据同步。 7. **异常处理**: 在数据库操作中如何处理可能发生的错误和异常。 ### 标签中的知识点 标签“Evc Sql CE 程序样例代码”与标题内容基本一致,强调了这部分内容是关于使用SQL CE的示例代码。标签通常用于标记和分类信息,方便在搜索引擎或者数据库中检索和识别特定内容。在实际应用中,开发者可以根据这样的标签快速找到相关的样例代码,以便于学习和参考。 ### 总结 根据标题、描述和标签,我们可以确定这篇内容是关于SQL Server Compact Edition的程序样例代码。由于缺乏具体的代码文件名列表,无法详细分析每个文件的内容。不过,上述内容已经概述了SQL CE的关键特性,以及开发者在参考样例代码时可能关注的知识点。 对于希望利用SQL CE进行数据库开发的程序员来说,样例代码是一个宝贵的资源,可以帮助他们快速理解和掌握如何在实际应用中运用该数据库技术。同时,理解SQL CE的特性、优势以及编程接口,将有助于开发者设计出更加高效、稳定的嵌入式数据库解决方案。
recommend-type

【浪潮FS6700交换机配置实战】:生产环境快速部署策略与技巧

# 1. 浪潮FS6700交换机概述 浪潮FS6700交换机是一款高端、具备高密度端口的全千兆交换机。它采用模块化设计,支持万兆上行,可提供灵活的端口组合和高带宽解决方案,满足了企业网络对于高性能、高可靠性的需求。浪潮FS6700交换机通常部署在企业网络的核心层或汇聚层,不仅提供强劲的网络数据交换能力,而且支持丰富的路由协议和安全特性,为中大型网络构建提供了强有力的保障。 接下来,我们将深
recommend-type

YOLO11训练批次参考

<think>我们正在讨论YOLOv11的训练批次设置。首先,需要明确的是,截至2023年10月,官方并没有发布YOLOv11版本。YOLO系列的最新版本是YOLOv8(由Ultralytics发布)。因此,这里可能指的是YOLO的某个变体或非官方版本。不过,我们可以基于YOLO系列的一般训练实践来讨论训练批次(batch size)的设置。 训练批次(batch size)是指在训练神经网络时,每次迭代中用于计算梯度并更新权重的样本数量。设置合适的batch size对训练效果和速度有重要影响。 ### 影响batch size选择的因素: 1. **硬件限制**:显存大小是主要限制因素
recommend-type

数据库考试复习必备五套习题精讲

根据给定的文件信息,本文将详细解释数据库习题相关知识点。首先,从标题中我们可以得知,该文件为数据库习题集,包含五套习题卷,非常适合用来准备考试。由于文件描述中提到考完试后才打算分享,说明这些习题具有一定的质量和难度,可以作为考试前的必备材料。 首先,我们来解释“数据库”这一核心概念。数据库是存储、管理、处理和检索信息的系统,它能够帮助我们有效地存储大量的数据,并在需要的时候快速访问。数据库管理系统(DBMS)是负责数据库创建、维护和操作的软件,常见的数据库管理系统包括MySQL、Oracle、Microsoft SQL Server、PostgreSQL和SQLite等。 数据库习题通常包括以下知识点: 1. 数据库设计:设计数据库时需要考虑实体-关系模型(ER模型)、规范化理论以及如何设计表结构。重点包括识别实体、确定实体属性、建立实体之间的关系以及表之间的关联。规范化是指将数据库表结构进行合理化分解,以减少数据冗余和提高数据一致性。 2. SQL语言:结构化查询语言(SQL)是用于管理数据库的标准计算机语言,它包括数据查询、数据操纵、数据定义和数据控制四个方面的功能。对于数据库习题来说,重点会涉及到以下SQL语句: - SELECT:用于从数据库中查询数据。 - INSERT、UPDATE、DELETE:用于向数据库中插入、更新或删除数据。 - CREATE TABLE、ALTER TABLE、DROP TABLE:用于创建、修改或删除表结构。 - JOIN:用于连接两个或多个表来查询跨越表的数据。 - GROUP BY 和 HAVING:用于对数据进行分组统计和筛选。 -事务处理:包括事务的ACID属性(原子性、一致性、隔离性、持久性)等。 3. 数据库操作:涉及实际操作数据库的过程,包括数据导入导出、备份与恢复、索引创建与优化等。这些内容能够帮助理解如何高效地管理数据。 4. 数据库安全:保障数据库不受未授权访问和破坏的机制,例如用户权限管理、视图、存储过程等安全措施。 5. 数据库优化:如何提升数据库的性能,包括查询优化、数据库配置优化、索引策略、系统资源监控等。 6. 数据库应用开发:如何利用数据库在应用程序中实现数据的持久化存储,如数据库连接、事务管理、数据访问对象(DAO)设计模式等。 7. 高级主题:涉及到复杂查询、数据库触发器、存储过程的编写和优化,以及可能包含的特定数据库系统的特定特性(如Oracle的PL/SQL编程等)。 由于文件名称列表只提供“数据库习题”这一个信息点,我们无法得知具体的习题内容和难度,但是可以肯定的是,这份习题集应该覆盖了上述所提到的知识点。对于考生来说,这些习题将帮助他们巩固理论知识,并且提高解决实际问题的能力,是考试前准备的有力工具。 在准备数据库相关的考试时,建议先从基础概念开始复习,然后逐步过渡到SQL语法和数据库设计的实践操作。在习题练习中,注意不要仅限于死记硬背,更重要的是理解每一个操作背后的逻辑和原理。如果可能的话,实际操作一个数据库,将理论知识应用到实践中去,这会帮助你更加深刻地理解数据库的工作机制。最后,反复练习模拟题,可以帮助你熟悉考试的题型和难度,提高考试时的应试技巧。
recommend-type

【浪潮FS6700交换机故障诊断与排除】:掌握这些方法,让你的网络稳定如初

# 1. 浪潮FS6700交换机故障诊断基础知识 在本章中,我们将探讨浪潮FS6700交换机故障诊断的基础知识,为后续章节中更深层次的理论和实践内容打下坚实的基础。我们将从以下几个方面入手,逐步深化对故障诊断的认识: ## 1.1 交换机故障诊断的重要性 交换机作为网络的核心设备,其稳定性和性能直接影响着整个网络的运行效率。因此,准确且快速地诊断和解决交换机故障是网
recommend-type

JVM内存整体结构图

<think>我们正在处理一个关于JVM内存结构的问题,用户要求获取详细图解。由于我们无法直接发送图片,但可以通过文字描述和引用相关资源来满足需求。 根据引用内容,我们可以总结JVM内存结构的主要部分: 1. 线程栈(Thread Stacks):每个线程创建时分配,存储局部变量和方法调用栈。 2. 堆(Heap):存储所有对象、实例变量和数组,被所有线程共享。堆又分为年轻代(Young Generation)和老年代(Old Generation)。 3. 非堆内存(Non-Heap Memory):包括方法区(Method Area)和运行时常量池(Runtime Constant
recommend-type

GEF应用实例:掌握界面设计的六步走

标题:“界面设计GEF应用实例”涉及的知识点: 1. GEF概述 GEF(Graphical Editing Framework)是基于Eclipse平台的一个图形编辑框架,用于创建交互式的图形编辑器。GEF通过分离图形表示与领域模型(Domain Model),使得开发者能够专注于界面设计而无需处理底层图形细节。它为图形编辑提供了三个核心组件:GEFEditingDomain、GEFEditPart和GEFEditPolicy,分别负责模型与视图的同步、视图部件的绘制与交互以及编辑策略的定义。 2. RCP(Rich Client Platform)简介 RCP是Eclipse技术的一个应用框架,它允许开发者快速构建功能丰富的桌面应用程序。RCP应用程序由一系列插件组成,这些插件可以共享Eclipse平台的核心功能,如工作台(Workbench)、帮助系统和更新机制等。RCP通过定义应用程序的界面布局、菜单和工具栏以及执行应用程序的生命周期管理,为开发高度可定制的应用程序提供了基础。 3. GEF与RCP的整合 在RCP应用程序中整合GEF,可以使用户在应用程序中拥有图形编辑的功能,这对于制作需要图形界面设计的工具尤其有用。RCP为GEF提供了一个运行环境,而GEF则通过提供图形编辑能力来增强RCP应用程序的功能。 4. 应用实例分析 文档中提到的“六个小例子”,可能分别代表了GEF应用的六个层次,由浅入深地介绍如何使用GEF构建图形编辑器。 - 第一个例子很可能是对GEF的入门介绍,包含如何设置GEF环境、创建一个基本的图形编辑器框架,并展示最简单的图形节点绘制功能。 - 随后的例子可能会增加对图形节点的编辑功能,如移动、缩放、旋转等操作。 - 更高级的例子可能会演示如何实现更复杂的图形节点关系,例如连接线的绘制和编辑,以及节点之间的依赖和关联。 - 高级例子中还可能包含对GEF扩展点的使用,以实现更高级的定制功能,如自定义图形节点的外观、样式以及编辑行为。 - 最后一个例子可能会介绍如何将GEF集成到RCP应用程序中,并展示如何利用RCP的功能特性来增强GEF编辑器的功能,如使用RCP的透视图切换、项目管理以及与其他RCP插件的交互等。 5. 插件的开发与配置 在构建GEF应用实例时,开发者需要熟悉插件的开发和配置。这包括对plugin.xml文件和MANIFEST.MF文件的配置,这两者共同定义了插件的依赖关系、执行入口点、扩展点以及与其他插件的交互关系等。 6. 用户交互和事件处理 在创建图形编辑器的过程中,用户交互和事件处理是核心部分。开发者需要了解如何捕获和处理用户在编辑器中产生的各种事件,如鼠标点击、拖拽、按键事件等,并将这些事件转换为编辑器的相应操作。 7. 模型-视图-控制器(MVC)设计模式 GEF采用了MVC设计模式,将业务逻辑(模型)、用户界面(视图)和控制逻辑(控制器)分离。开发者需要理解MVC模式的工作原理,以及如何在GEF中应用这一模式来实现图形编辑器的各个部分。 8. 自定义绘图和渲染技术 在高级应用实例中,开发者可能需要自定义图形节点的绘制方法,以及图形的渲染技术。这通常涉及对Eclipse GEF的图形API的理解和使用,例如使用Draw2D或Gef图形库中的类和接口来实现定制的渲染效果。 通过这些知识点的讲解和实例的展示,读者可以逐步学会如何使用GEF构建图形编辑器,并在RCP平台上进行集成和扩展,从而创建出功能丰富、可定制和交互性良好的图形应用程序。
recommend-type

掌握Python FloodRouting:构建洪水预测模型的终极指南

# 摘要 随着气候变化和极端天气事件的增多,洪水预测成为防范灾害和减轻其影响的关键技术。本文介绍了Python FloodRouting软件包,详细阐述了洪水预测模型的理论基础,包括数学原理、数据收集与预处理的重要性。文章继续探讨了Python FloodRouting的安装、环境配置以及构建洪水预测模型的实践操作。通过集成学习和模型融合技术,介绍了如何构建实时洪水预
recommend-type

Python批量修改文件后缀

<think>我们有一个明确的需求:使用Python批量修改文件扩展名(后缀名)。根据用户提供的引用,特别是引用[2]和引用[3],我们可以得到两种不同的情况: 1. 引用[2]:将没有后缀的文件批量添加后缀(例如.txt)。 2. 引用[3]:将指定后缀名的文件批量修改为另一种后缀名。 因此,我们需要根据用户的具体需求来编写脚本。但用户的问题描述是“批量修改文件扩展名”,这通常指的是改变已有文件的后缀名,但也可以包括给无后缀文件添加后缀。 我们可以设计一个灵活的脚本,能够处理两种情况: - 情况1:将指定目录下所有指定旧后缀名的文件改为新后缀名(例如将.jpg改为.png)。 - 情况2