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LLM 微调参数
特定于任务的参数
用于不同训练器的长度参数可能不同。有些需要比其他训练器更多的上下文。
- block_size: 这是最大序列长度或文本块的长度。设置为 -1 会自动确定块大小。默认值为 -1。
- model_max_length: 设置模型在单个批次中处理的最大长度，这会影响性能和内存使用情况。默认值为 1024
- max_prompt_length: 指定用于训练的提示的最大长度，这与需要初始上下文输入的任务特别相关。仅用于
orpo和dpo训练器。 - max_completion_length: 用于完成的长度，对于 orpo: 仅限于编码器-解码器模型。对于 dpo，它是完成文本的长度。
注意:
- 块大小不能大于 model_max_length！
- max_prompt_length 不能大于 model_max_length！
- max_prompt_length 不能大于 block_size！
- max_completion_length 不能大于 model_max_length！
- max_completion_length 不能大于 block_size！
注意: 不遵循这些约束会导致错误/nan 损失。
通用训练器
--add_eos_token, --add-eos-token
Toggle whether to automatically add an End Of Sentence (EOS) token at the end of texts, which can be critical for certain
types of models like language models. Only used for `default` trainer
--block_size BLOCK_SIZE, --block-size BLOCK_SIZE
Specify the block size for processing sequences. This is maximum sequence length or length of one block of text. Setting to
-1 determines block size automatically. Default is -1.
--model_max_length MODEL_MAX_LENGTH, --model-max-length MODEL_MAX_LENGTH
Set the maximum length for the model to process in a single batch, which can affect both performance and memory usage.
Default is 1024SFT 训练器
--block_size BLOCK_SIZE, --block-size BLOCK_SIZE
Specify the block size for processing sequences. This is maximum sequence length or length of one block of text. Setting to
-1 determines block size automatically. Default is -1.
--model_max_length MODEL_MAX_LENGTH, --model-max-length MODEL_MAX_LENGTH
Set the maximum length for the model to process in a single batch, which can affect both performance and memory usage.
Default is 1024奖励训练器
--block_size BLOCK_SIZE, --block-size BLOCK_SIZE
Specify the block size for processing sequences. This is maximum sequence length or length of one block of text. Setting to
-1 determines block size automatically. Default is -1.
--model_max_length MODEL_MAX_LENGTH, --model-max-length MODEL_MAX_LENGTH
Set the maximum length for the model to process in a single batch, which can affect both performance and memory usage.
Default is 1024DPO 训练器
--dpo-beta DPO_BETA, --dpo-beta DPO_BETA
Beta for DPO trainer
--model-ref MODEL_REF
Reference model to use for DPO when not using PEFT
--block_size BLOCK_SIZE, --block-size BLOCK_SIZE
Specify the block size for processing sequences. This is maximum sequence length or length of one block of text. Setting to
-1 determines block size automatically. Default is -1.
--model_max_length MODEL_MAX_LENGTH, --model-max-length MODEL_MAX_LENGTH
Set the maximum length for the model to process in a single batch, which can affect both performance and memory usage.
Default is 1024
--max_prompt_length MAX_PROMPT_LENGTH, --max-prompt-length MAX_PROMPT_LENGTH
Specify the maximum length for prompts used in training, particularly relevant for tasks requiring initial contextual input.
Used only for `orpo` trainer.
--max_completion_length MAX_COMPLETION_LENGTH, --max-completion-length MAX_COMPLETION_LENGTH
Completion length to use, for orpo: encoder-decoder models onlyORPO 训练器
--block_size BLOCK_SIZE, --block-size BLOCK_SIZE
Specify the block size for processing sequences. This is maximum sequence length or length of one block of text. Setting to
-1 determines block size automatically. Default is -1.
--model_max_length MODEL_MAX_LENGTH, --model-max-length MODEL_MAX_LENGTH
Set the maximum length for the model to process in a single batch, which can affect both performance and memory usage.
Default is 1024
--max_prompt_length MAX_PROMPT_LENGTH, --max-prompt-length MAX_PROMPT_LENGTH
Specify the maximum length for prompts used in training, particularly relevant for tasks requiring initial contextual input.
Used only for `orpo` trainer.
--max_completion_length MAX_COMPLETION_LENGTH, --max-completion-length MAX_COMPLETION_LENGTH
Completion length to use, for orpo: encoder-decoder models only通用参数
--batch-size BATCH_SIZE, --train-batch-size BATCH_SIZE
Training batch size to use
--seed SEED Random seed for reproducibility
--epochs EPOCHS Number of training epochs
--gradient_accumulation GRADIENT_ACCUMULATION, --gradient-accumulation GRADIENT_ACCUMULATION
Gradient accumulation steps
--disable_gradient_checkpointing, --disable-gradient-checkpointing, --disable-gc
Disable gradient checkpointing
--lr LR Learning rate
--log {none,wandb,tensorboard}
Use experiment tracking
--warmup_ratio WARMUP_RATIO, --warmup-ratio WARMUP_RATIO
Set the proportion of training allocated to warming up the learning rate, which can enhance model stability and performance
at the start of training. Default is 0.1
--optimizer OPTIMIZER
Choose the optimizer algorithm for training the model. Different optimizers can affect the training speed and model
performance. 'adamw_torch' is used by default.
--scheduler SCHEDULER
Select the learning rate scheduler to adjust the learning rate based on the number of epochs. 'linear' decreases the
learning rate linearly from the initial lr set. Default is 'linear'. Try 'cosine' for a cosine annealing schedule.
--weight_decay WEIGHT_DECAY, --weight-decay WEIGHT_DECAY
Define the weight decay rate for regularization, which helps prevent overfitting by penalizing larger weights. Default is
0.0
--max_grad_norm MAX_GRAD_NORM, --max-grad-norm MAX_GRAD_NORM
Set the maximum norm for gradient clipping, which is critical for preventing gradients from exploding during
backpropagation. Default is 1.0.
--peft, --use-peft Enable LoRA-PEFT
--lora_r LORA_R, --lora-r LORA_R
Set the 'r' parameter for Low-Rank Adaptation (LoRA). Default is 16.
--lora_alpha LORA_ALPHA, --lora-alpha LORA_ALPHA
Specify the 'alpha' parameter for LoRA. Default is 32.
--lora_dropout LORA_DROPOUT, --lora-dropout LORA_DROPOUT
Set the dropout rate within the LoRA layers to help prevent overfitting during adaptation. Default is 0.05.
--logging_steps LOGGING_STEPS, --logging-steps LOGGING_STEPS
Determine how often to log training progress in terms of steps. Setting it to '-1' determines logging steps automatically.
--eval_strategy {epoch,steps,no}, --eval-strategy {epoch,steps,no}
Choose how frequently to evaluate the model's performance, with 'epoch' as the default, meaning at the end of each training
epoch
--save_total_limit SAVE_TOTAL_LIMIT, --save-total-limit SAVE_TOTAL_LIMIT
Limit the total number of saved model checkpoints to manage disk usage effectively. Default is to save only the latest
checkpoint
--auto_find_batch_size, --auto-find-batch-size
Automatically determine the optimal batch size based on system capabilities to maximize efficiency.
--mixed_precision {fp16,bf16,None}, --mixed-precision {fp16,bf16,None}
Choose the precision mode for training to optimize performance and memory usage. Options are 'fp16', 'bf16', or None for
default precision. Default is None.
--quantization {int4,int8,None}, --quantization {int4,int8,None}
Choose the quantization level to reduce model size and potentially increase inference speed. Options include 'int4', 'int8',
or None. Enabling requires --peft
--trainer {default,dpo,sft,orpo,reward}
Trainer type to use
--target_modules TARGET_MODULES, --target-modules TARGET_MODULES
Identify specific modules within the model architecture to target with adaptations or optimizations, such as LoRA. Comma
separated list of module names. Default is 'all-linear'.
--merge_adapter, --merge-adapter
Use this flag to merge PEFT adapter with the model
--use_flash_attention_2, --use-flash-attention-2, --use-fa2
Use flash attention 2
--chat_template {tokenizer,chatml,zephyr,None}, --chat-template {tokenizer,chatml,zephyr,None}
Apply a specific template for chat-based interactions, with options including 'tokenizer', 'chatml', 'zephyr', or None. This
setting can shape the model's conversational behavior.
--padding {left,right,None}, --padding {left,right,None}
Specify the padding direction for sequences, critical for models sensitive to input alignment. Options include 'left',
'right', or None