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support llama pro #2338 , add rslora

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Former-commit-id: 40d659b7f30dd5a004703c176ec1f22dc864e505
This commit is contained in:
hiyouga
2024-02-15 02:27:36 +08:00
parent b403f8d8a8
commit 596b6828cb
24 changed files with 438 additions and 203 deletions
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44
src/llmtuner/hparams/data_args.py
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@@ -9,30 +9,40 @@ class DataArguments:
"""
template: Optional[str] = field(
default=None, metadata={"help": "Which template to use for constructing prompts in training and inference."}
default=None,
metadata={"help": "Which template to use for constructing prompts in training and inference."},
)
dataset: Optional[str] = field(
default=None,
metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."},
)
dataset_dir: Optional[str] = field(
default="data", metadata={"help": "Path to the folder containing the datasets."}
default="data",
metadata={"help": "Path to the folder containing the datasets."},
)
split: Optional[str] = field(
default="train", metadata={"help": "Which dataset split to use for training and evaluation."}
default="train",
metadata={"help": "Which dataset split to use for training and evaluation."},
)
cutoff_len: Optional[int] = field(
default=1024, metadata={"help": "The cutoff length of the model inputs after tokenization."}
default=1024,
metadata={"help": "The cutoff length of the model inputs after tokenization."},
)
reserved_label_len: Optional[int] = field(
default=1, metadata={"help": "The minimum cutoff length reserved for label after tokenization."}
default=1,
metadata={"help": "The minimum cutoff length reserved for label after tokenization."},
)
train_on_prompt: Optional[bool] = field(
default=False, metadata={"help": "Whether to disable the mask on the prompt or not."}
default=False,
metadata={"help": "Whether to disable the mask on the prompt or not."},
)
streaming: Optional[bool] = field(
default=False,
metadata={"help": "Enable dataset streaming."},
)
streaming: Optional[bool] = field(default=False, metadata={"help": "Enable dataset streaming."})
buffer_size: Optional[int] = field(
default=16384, metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."}
default=16384,
metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."},
)
mix_strategy: Optional[Literal["concat", "interleave_under", "interleave_over"]] = field(
default="concat",
@@ -43,13 +53,16 @@ class DataArguments:
metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
)
overwrite_cache: Optional[bool] = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets."}
default=False,
metadata={"help": "Overwrite the cached training and evaluation sets."},
)
preprocessing_num_workers: Optional[int] = field(
default=None, metadata={"help": "The number of processes to use for the preprocessing."}
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_samples: Optional[int] = field(
default=None, metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."}
default=None,
metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."},
)
eval_num_beams: Optional[int] = field(
default=None,
@@ -62,13 +75,16 @@ class DataArguments:
},
)
val_size: Optional[float] = field(
default=0, metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."}
default=0,
metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."},
)
sft_packing: Optional[bool] = field(
default=False, metadata={"help": "Packing the questions and answers in the supervised fine-tuning stage."}
default=False,
metadata={"help": "Packing the questions and answers in the supervised fine-tuning stage."},
)
cache_path: Optional[str] = field(
default=None, metadata={"help": "Path to save or load the preprocessed datasets."}
default=None,
metadata={"help": "Path to save or load the preprocessed datasets."},
)
def __post_init__(self):

34
src/llmtuner/hparams/evaluation_args.py
View File

@@ -11,15 +11,33 @@ class EvaluationArguments:
Arguments pertaining to specify the evaluation parameters.
"""
task: str = field(metadata={"help": "Name of the evaluation task."})
task_dir: Optional[str] = field(
default="evaluation", metadata={"help": "Path to the folder containing the evaluation datasets."}
task: str = field(
metadata={"help": "Name of the evaluation task."},
)
task_dir: Optional[str] = field(
default="evaluation",
metadata={"help": "Path to the folder containing the evaluation datasets."},
)
batch_size: Optional[int] = field(
default=4,
metadata={"help": "The batch size per GPU for evaluation."},
)
seed: Optional[int] = field(
default=42,
metadata={"help": "Random seed to be used with data loaders."},
)
lang: Optional[Literal["en", "zh"]] = field(
default="en",
metadata={"help": "Language used at evaluation."},
)
n_shot: Optional[int] = field(
default=5,
metadata={"help": "Number of examplars for few-shot learning."},
)
save_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to save the evaluation results."},
)
batch_size: Optional[int] = field(default=4, metadata={"help": "The batch size per GPU for evaluation."})
seed: Optional[int] = field(default=42, metadata={"help": "Random seed to be used with data loaders."})
lang: Optional[Literal["en", "zh"]] = field(default="en", metadata={"help": "Language used at evaluation."})
n_shot: Optional[int] = field(default=5, metadata={"help": "Number of examplars for few-shot learning."})
save_dir: Optional[str] = field(default=None, metadata={"help": "Path to save the evaluation results."})
download_mode: Optional[DownloadMode] = field(
default=DownloadMode.REUSE_DATASET_IF_EXISTS,
metadata={"help": "Download mode used for the evaluation datasets."},

120
src/llmtuner/hparams/finetuning_args.py
View File

@@ -10,20 +10,25 @@ class FreezeArguments:
"""
name_module_trainable: Optional[str] = field(
default="mlp",
default=None,
metadata={
"help": 'Name of trainable modules for partial-parameter (freeze) fine-tuning. \
Use commas to separate multiple modules. \
LLaMA choices: ["mlp", "self_attn"], \
BLOOM & Falcon & ChatGLM choices: ["mlp", "self_attention"], \
Qwen choices: ["mlp", "attn"], \
Phi choices: ["mlp", "mixer"], \
InternLM2 choices: ["feed_forward", "attention"], \
Others choices: the same as LLaMA.'
"help": """Name of trainable modules for partial-parameter (freeze) fine-tuning. \
Use commas to separate multiple modules. \
Use "all" to specify all the available modules. \
LLaMA choices: ["mlp", "self_attn"], \
BLOOM & Falcon & ChatGLM choices: ["mlp", "self_attention"], \
Qwen choices: ["mlp", "attn"], \
InternLM2 choices: ["feed_forward", "attention"], \
Others choices: the same as LLaMA."""
},
)
num_layer_trainable: Optional[int] = field(
default=3, metadata={"help": "The number of trainable layers for partial-parameter (freeze) fine-tuning."}
default=3,
metadata={"help": "The number of trainable layers for partial-parameter (freeze) fine-tuning."},
)
use_llama_pro: Optional[bool] = field(
default=False,
metadata={"help": "Whether or not to use llama pro for partial-parameter (freeze) fine-tuning."},
)
@@ -40,27 +45,42 @@ class LoraArguments:
},
)
lora_alpha: Optional[int] = field(
default=None, metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2)."}
default=None,
metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2)."},
)
lora_dropout: Optional[float] = field(
default=0.0,
metadata={"help": "Dropout rate for the LoRA fine-tuning."},
)
lora_rank: Optional[int] = field(
default=8,
metadata={"help": "The intrinsic dimension for LoRA fine-tuning."},
)
lora_dropout: Optional[float] = field(default=0.0, metadata={"help": "Dropout rate for the LoRA fine-tuning."})
lora_rank: Optional[int] = field(default=8, metadata={"help": "The intrinsic dimension for LoRA fine-tuning."})
lora_target: Optional[str] = field(
default=None,
metadata={
"help": 'Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
LLaMA choices: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], \
BLOOM & Falcon & ChatGLM choices: ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"], \
Baichuan choices: ["W_pack", "o_proj", "gate_proj", "up_proj", "down_proj"], \
Qwen choices: ["c_attn", "attn.c_proj", "w1", "w2", "mlp.c_proj"], \
Phi choices: ["Wqkv", "out_proj", "fc1", "fc2"], \
Others choices: the same as LLaMA.'
"help": """Name(s) of target modules to apply LoRA. \
Use commas to separate multiple modules. \
Use "all" to specify all the available modules. \
LLaMA choices: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], \
BLOOM & Falcon & ChatGLM choices: ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"], \
Baichuan choices: ["W_pack", "o_proj", "gate_proj", "up_proj", "down_proj"], \
Qwen choices: ["c_attn", "attn.c_proj", "w1", "w2", "mlp.c_proj"], \
InternLM2 choices: ["wqkv", "wo", "w1", "w2", "w3"], \
Others choices: the same as LLaMA."""
},
)
lora_bf16_mode: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to train lora adapters in bf16 precision."}
default=False,
metadata={"help": "Whether or not to train lora adapters in bf16 precision."},
)
use_rslora: Optional[bool] = field(
default=False,
metadata={"help": "Whether or not to use the rank stabilization scaling factor for LoRA layer."},
)
create_new_adapter: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."}
default=False,
metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},
)
@@ -70,49 +90,65 @@ class RLHFArguments:
Arguments pertaining to the PPO and DPO training.
"""
dpo_beta: Optional[float] = field(default=0.1, metadata={"help": "The beta parameter for the DPO loss."})
dpo_beta: Optional[float] = field(
default=0.1,
metadata={"help": "The beta parameter for the DPO loss."},
)
dpo_loss: Optional[Literal["sigmoid", "hinge", "ipo", "kto"]] = field(
default="sigmoid", metadata={"help": "The type of DPO loss to use."}
default="sigmoid",
metadata={"help": "The type of DPO loss to use."},
)
dpo_ftx: Optional[float] = field(
default=0, metadata={"help": "The supervised fine-tuning loss coefficient in DPO training."}
default=0,
metadata={"help": "The supervised fine-tuning loss coefficient in DPO training."},
)
ppo_buffer_size: Optional[int] = field(
default=1,
metadata={"help": "The number of mini-batches to make experience buffer in a PPO optimization step."},
)
ppo_epochs: Optional[int] = field(
default=4, metadata={"help": "The number of epochs to perform in a PPO optimization step."}
default=4,
metadata={"help": "The number of epochs to perform in a PPO optimization step."},
)
ppo_logger: Optional[str] = field(
default=None, metadata={"help": 'Log with either "wandb" or "tensorboard" in PPO training.'}
default=None,
metadata={"help": 'Log with either "wandb" or "tensorboard" in PPO training.'},
)
ppo_score_norm: Optional[bool] = field(
default=False, metadata={"help": "Use score normalization in PPO training."}
default=False,
metadata={"help": "Use score normalization in PPO training."},
)
ppo_target: Optional[float] = field(
default=6.0, metadata={"help": "Target KL value for adaptive KL control in PPO training."}
default=6.0,
metadata={"help": "Target KL value for adaptive KL control in PPO training."},
)
ppo_whiten_rewards: Optional[bool] = field(
default=False, metadata={"help": "Whiten the rewards before compute advantages in PPO training."}
default=False,
metadata={"help": "Whiten the rewards before compute advantages in PPO training."},
)
ref_model: Optional[str] = field(
default=None, metadata={"help": "Path to the reference model used for the PPO or DPO training."}
default=None,
metadata={"help": "Path to the reference model used for the PPO or DPO training."},
)
ref_model_adapters: Optional[str] = field(
default=None, metadata={"help": "Path to the adapters of the reference model."}
default=None,
metadata={"help": "Path to the adapters of the reference model."},
)
ref_model_quantization_bit: Optional[int] = field(
default=None, metadata={"help": "The number of bits to quantize the reference model."}
default=None,
metadata={"help": "The number of bits to quantize the reference model."},
)
reward_model: Optional[str] = field(
default=None, metadata={"help": "Path to the reward model used for the PPO training."}
default=None,
metadata={"help": "Path to the reward model used for the PPO training."},
)
reward_model_adapters: Optional[str] = field(
default=None, metadata={"help": "Path to the adapters of the reward model."}
default=None,
metadata={"help": "Path to the adapters of the reward model."},
)
reward_model_quantization_bit: Optional[int] = field(
default=None, metadata={"help": "The number of bits to quantize the reward model."}
default=None,
metadata={"help": "The number of bits to quantize the reward model."},
)
reward_model_type: Optional[Literal["lora", "full", "api"]] = field(
default="lora",
@@ -127,16 +163,20 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments):
"""
stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
default="sft", metadata={"help": "Which stage will be performed in training."}
default="sft",
metadata={"help": "Which stage will be performed in training."},
)
finetuning_type: Optional[Literal["lora", "freeze", "full"]] = field(
default="lora", metadata={"help": "Which fine-tuning method to use."}
default="lora",
metadata={"help": "Which fine-tuning method to use."},
)
disable_version_checking: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to disable version checking."}
default=False,
metadata={"help": "Whether or not to disable version checking."},
)
plot_loss: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to save the training loss curves."}
default=False,
metadata={"help": "Whether or not to save the training loss curves."},
)
def __post_init__(self):

15
src/llmtuner/hparams/generating_args.py
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@@ -9,10 +9,12 @@ class GeneratingArguments:
"""
do_sample: Optional[bool] = field(
default=True, metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."}
default=True,
metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."},
)
temperature: Optional[float] = field(
default=0.95, metadata={"help": "The value used to modulate the next token probabilities."}
default=0.95,
metadata={"help": "The value used to modulate the next token probabilities."},
)
top_p: Optional[float] = field(
default=0.7,
@@ -25,7 +27,8 @@ class GeneratingArguments:
metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
)
num_beams: Optional[int] = field(
default=1, metadata={"help": "Number of beams for beam search. 1 means no beam search."}
default=1,
metadata={"help": "Number of beams for beam search. 1 means no beam search."},
)
max_length: Optional[int] = field(
default=512,
@@ -36,10 +39,12 @@ class GeneratingArguments:
metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
)
repetition_penalty: Optional[float] = field(
default=1.0, metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."}
default=1.0,
metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."},
)
length_penalty: Optional[float] = field(
default=1.0, metadata={"help": "Exponential penalty to the length that is used with beam-based generation."}
default=1.0,
metadata={"help": "Exponential penalty to the length that is used with beam-based generation."},
)
def to_dict(self) -> Dict[str, Any]:

78
src/llmtuner/hparams/model_args.py
View File

@@ -9,10 +9,13 @@ class ModelArguments:
"""
model_name_or_path: str = field(
metadata={"help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."}
metadata={
"help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
},
)
adapter_name_or_path: Optional[str] = field(
default=None, metadata={"help": "Path to the adapter weight or identifier from huggingface.co/models."}
default=None,
metadata={"help": "Path to the adapter weight or identifier from huggingface.co/models."},
)
cache_dir: Optional[str] = field(
default=None,
@@ -23,7 +26,8 @@ class ModelArguments:
metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
)
resize_vocab: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."}
default=False,
metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."},
)
split_special_tokens: Optional[bool] = field(
default=False,
@@ -34,60 +38,88 @@ class ModelArguments:
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
quantization_bit: Optional[int] = field(
default=None, metadata={"help": "The number of bits to quantize the model."}
default=None,
metadata={"help": "The number of bits to quantize the model."},
)
quantization_type: Optional[Literal["fp4", "nf4"]] = field(
default="nf4", metadata={"help": "Quantization data type to use in int4 training."}
default="nf4",
metadata={"help": "Quantization data type to use in int4 training."},
)
double_quantization: Optional[bool] = field(
default=True, metadata={"help": "Whether or not to use double quantization in int4 training."}
default=True,
metadata={"help": "Whether or not to use double quantization in int4 training."},
)
rope_scaling: Optional[Literal["linear", "dynamic"]] = field(
default=None, metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."}
default=None,
metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
)
flash_attn: Optional[bool] = field(
default=False, metadata={"help": "Enable FlashAttention-2 for faster training."}
default=False,
metadata={"help": "Enable FlashAttention-2 for faster training."},
)
shift_attn: Optional[bool] = field(
default=False, metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."}
default=False,
metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
)
use_unsloth: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."}
default=False,
metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
)
disable_gradient_checkpointing: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to disable gradient checkpointing."}
default=False,
metadata={"help": "Whether or not to disable gradient checkpointing."},
)
upcast_layernorm: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to upcast the layernorm weights in fp32."}
default=False,
metadata={"help": "Whether or not to upcast the layernorm weights in fp32."},
)
upcast_lmhead_output: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to upcast the output of lm_head in fp32."}
default=False,
metadata={"help": "Whether or not to upcast the output of lm_head in fp32."},
)
hf_hub_token: Optional[str] = field(
default=None,
metadata={"help": "Auth token to log in with Hugging Face Hub."},
)
ms_hub_token: Optional[str] = field(
default=None,
metadata={"help": "Auth token to log in with ModelScope Hub."},
)
hf_hub_token: Optional[str] = field(default=None, metadata={"help": "Auth token to log in with Hugging Face Hub."})
ms_hub_token: Optional[str] = field(default=None, metadata={"help": "Auth token to log in with ModelScope Hub."})
export_dir: Optional[str] = field(
default=None, metadata={"help": "Path to the directory to save the exported model."}
default=None,
metadata={"help": "Path to the directory to save the exported model."},
)
export_size: Optional[int] = field(
default=1, metadata={"help": "The file shard size (in GB) of the exported model."}
default=1,
metadata={"help": "The file shard size (in GB) of the exported model."},
)
export_quantization_bit: Optional[int] = field(
default=None, metadata={"help": "The number of bits to quantize the exported model."}
default=None,
metadata={"help": "The number of bits to quantize the exported model."},
)
export_quantization_dataset: Optional[str] = field(
default=None, metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."}
default=None,
metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."},
)
export_quantization_nsamples: Optional[int] = field(
default=128, metadata={"help": "The number of samples used for quantization."}
default=128,
metadata={"help": "The number of samples used for quantization."},
)
export_quantization_maxlen: Optional[int] = field(
default=1024, metadata={"help": "The maximum length of the model inputs used for quantization."}
default=1024,
metadata={"help": "The maximum length of the model inputs used for quantization."},
)
export_legacy_format: Optional[bool] = field(
default=False, metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."}
default=False,
metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."},
)
export_hub_model_id: Optional[str] = field(
default=None, metadata={"help": "The name of the repository if push the model to the Hugging Face hub."}
default=None,
metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
)
print_param_status: Optional[bool] = field(
default=False,
metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
)
def __post_init__(self):

34
src/llmtuner/hparams/parser.py
View File

@@ -30,12 +30,15 @@ _EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArgu
_EVAL_CLS = Tuple[ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
def _check_dependencies():
require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")
require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
require_version("peft>=0.7.0", "To fix: pip install peft>=0.7.0")
require_version("trl>=0.7.6", "To fix: pip install trl>=0.7.6")
def _check_dependencies(disabled: bool) -> None:
if disabled:
logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
else:
require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")
require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
require_version("peft>=0.8.2", "To fix: pip install peft>=0.8.2")
require_version("trl>=0.7.6", "To fix: pip install trl>=0.7.6")
def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
@@ -130,6 +133,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if training_args.do_train and training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True while training.")
if (
training_args.do_train
and finetuning_args.finetuning_type == "freeze"
and finetuning_args.name_module_trainable is None
):
raise ValueError("Please specify `name_module_trainable` in Freeze training.")
if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None:
raise ValueError("Please specify `lora_target` in LoRA training.")
@@ -137,9 +147,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("Install Unsloth: https://github.com/unslothai/unsloth")
_verify_model_args(model_args, finetuning_args)
if not finetuning_args.disable_version_checking:
_check_dependencies()
_check_dependencies(disabled=finetuning_args.disable_version_checking)
if (
training_args.do_train
@@ -240,13 +248,11 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
_set_transformers_logging()
_verify_model_args(model_args, finetuning_args)
_check_dependencies(disabled=finetuning_args.disable_version_checking)
if data_args.template is None:
raise ValueError("Please specify which `template` to use.")
if not finetuning_args.disable_version_checking:
_check_dependencies()
return model_args, data_args, finetuning_args, generating_args
@@ -255,13 +261,11 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
_set_transformers_logging()
_verify_model_args(model_args, finetuning_args)
_check_dependencies(disabled=finetuning_args.disable_version_checking)
if data_args.template is None:
raise ValueError("Please specify which `template` to use.")
if not finetuning_args.disable_version_checking:
_check_dependencies()
transformers.set_seed(eval_args.seed)
return model_args, data_args, eval_args, finetuning_args