# Command Line Arguments

## Table of Contents

- [sft Parameters](#sft-parameters)
- [dpo Parameters](#dpo-parameters)
- [merge-lora infer Parameters](#merge-lora-infer-parameters)
- [export Parameters](#export-parameters)
- [eval Parameters](#eval-parameters)
- [app-ui Parameters](#app-ui-parameters)
- [deploy Parameters](#deploy-parameters)

## sft Parameters
- `--model_type`: Represents the selected model type, default is `None`. `model_type` specifies the default `lora_target_modules`, `template_type`, and other information for the corresponding model. You can fine-tune by specifying only `model_type`. The corresponding `model_id_or_path` will use default settings, and the model will be downloaded from ModelScope and use the default cache path. One of model_type and model_id_or_path must be specified. You can see the list of available `model_type` [here](Supported-models-datasets.md#Models). You can set the `USE_HF` environment variable to control downloading models and datasets from the HF Hub, see [HuggingFace Ecosystem Compatibility Documentation](Compat-HF.md).
- `--model_id_or_path`: Represents the `model_id` in the ModelScope/HuggingFace Hub or a local path for the model, default is `None`. If the provided `model_id_or_path` has already been registered, the `model_type` will be inferred based on the `model_id_or_path`. If it has not been registered, both `model_type` and `model_id_or_path` must be specified, e.g. `--model_type <model_type> --model_id_or_path <model_id_or_path>`.
- `--model_revision`: The version number corresponding to `model_id` on ModelScope Hub, default is `None`. If `model_revision` is `None`, use the revision registered in `MODEL_MAPPING`. Otherwise, force use of the `model_revision` passed from command line.
- `--local_repo_path`: Some models rely on a GitHub repo for loading. To avoid network issues during `git clone`, you can directly use the local repo. This parameter requires input of the local repo path, and defaults to `None`. These models include:
  - mPLUG-Owl model: `https://github.com/X-PLUG/mPLUG-Owl`
  - DeepSeek-VL model: `https://github.com/deepseek-ai/DeepSeek-VL`
  - YI-VL model: `https://github.com/01-ai/Yi`
  - LLAVA model: `https://github.com/haotian-liu/LLaVA.git`
- `--sft_type`: Fine-tuning method, default is `'lora'`. Options include: 'lora', 'full', 'longlora', 'adalora', 'ia3', 'llamapro', 'adapter', 'vera', 'boft'. If using qlora, you need to set `--sft_type lora --quantization_bit 4`.
- `--packing`: pack the dataset length to `max-length`, default `False`.
- `--freeze_parameters`: When sft_type is set to 'full', freeze the bottommost parameters of the model. Range is 0. ~ 1., default is `0.`. This provides a compromise between lora and full fine-tuning.
- `--additional_trainable_parameters`: In addition to freeze_parameters, only allowed when sft_type is 'full', default is `[]`. For example, if you want to train embedding layer in addition to 50% of parameters, you can set `--freeze_parameters 0.5 --additional_trainable_parameters transformer.wte`, all parameters starting with `transformer.wte` will be activated.
- `--tuner_backend`: Backend support for lora, qlora, default is `'peft'`. Options include: 'swift', 'peft', 'unsloth'.
- `--template_type`: Type of dialogue template used, default is `'AUTO'`, i.e. look up `template` in `MODEL_MAPPING` based on `model_type`. Available `template_type` options can be found in `TEMPLATE_MAPPING.keys()`.
- `--output_dir`: Directory to store ckpt, default is `'output'`. We will append `model_type` and fine-tuning version number to this directory, allowing users to do multiple comparative experiments on different models without changing the `output_dir` command line argument. If you don't want to append this content, specify `--add_output_dir_suffix false`.
- `--add_output_dir_suffix`: Default is `True`, indicating that a suffix of `model_type` and fine-tuning version number will be appended to the `output_dir` directory. Set to `False` to avoid this behavior.
- `--ddp_backend`: Backend support for distributed training, default is `None`. Options include: 'nccl', 'gloo', 'mpi', 'ccl'.
- `--seed`: Global seed, default is `42`. Used to reproduce training results.
- `--resume_from_checkpoint`: For resuming training from checkpoint, default is `None`. You can set this to the path of a checkpoint, e.g. `'output/qwen-7b-chat/vx-xxx/checkpoint-xxx'`, to resume training.
- `--dtype`: torch_dtype when loading base model, default is `'AUTO'`, i.e. intelligently select dtype: if machine does not support bf16, use fp16; if `MODEL_MAPPING` specifies torch_dtype for corresponding model, use its dtype; otherwise use bf16. Options include: 'bf16', 'fp16', 'fp32'.
- `--dataset`: Used to select the training dataset, default is `[]`. You can see the list of available datasets [here](Supported-models-datasets.md#Datasets). If you need to train with multiple datasets, you can use ',' or ' ' to separate them, for example: `--dataset alpaca-en,alpaca-zh` or `--dataset alpaca-en alpaca-zh`. It supports Modelscope Hub/HuggingFace Hub/local paths, subset selection, and dataset sampling. The specified format for each dataset is as follows: `[HF or MS::]{dataset_name} or {dataset_id} or {dataset_path}[:subset1/subset2/...][#dataset_sample]`. The simplest case requires specifying only dataset_name, dataset_id, or dataset_path. Customizing datasets can be found in the [Customizing and Extending Datasets document](Customization.md#custom-dataset)
  - Supports MS and HF hub, as well as dataset_sample. For example, 'MS::alpaca-zh#2000', 'HF::jd-sentiment-zh#2000' (the default hub used is controlled by the `USE_UF` environment variable, default is MS).
  - More fine-grained control over subsets: It uses the subsets specified during registration by default (if not specified during registration, it uses 'default'). For example, 'sharegpt-gpt4'. If subsets are specified, it uses the corresponding subset of the dataset. For example, 'sharegpt-gpt4:default/V3_format#2000'. Separated by '/'.
  - Support for dataset_id. For example, 'AI-ModelScope/alpaca-gpt4-data-zh#2000', 'HF::llm-wizard/alpaca-gpt4-data-zh#2000', 'hurner/alpaca-gpt4-data-zh#2000', 'HF::shibing624/alpaca-zh#2000'. If the dataset_id has been registered, it will use the preprocessing function, subsets, split, etc. specified during registration. Otherwise, it will use `SmartPreprocessor`, support 5 dataset formats, and use 'default' subsets, with split set to 'train'. The supported dataset formats can be found in the [Customizing and Extending Datasets document](Customization.md#custom-dataset).
  - Support for dataset_path. For example, '1.jsonl#5000' (if it is a relative path, it is relative to the running directory).
- `--val_dataset`: Specify separate validation datasets with the same format of the `dataset` argument, default is `[]`. If using `val_dataset`, the `dataset_test_ratio` will be ignored.
- `--dataset_seed`: Seed for dataset processing, default is `42`. Exists as random_state, does not affect global seed.
- `--dataset_test_ratio`: Used to specify the ratio for splitting the sub-dataset into training and validation sets. The default value is `0.01`. If `--val_dataset` is set, this parameter becomes ineffective.
- `--train_dataset_sample`: The number of samples for the training dataset, default is `-1`, which means using the complete training dataset for training. This parameter is deprecated, please use `--dataset {dataset_name}#{dataset_sample}` instead.
- `--val_dataset_sample`: Used to sample the validation set, with a default value of `None`, which automatically selects a suitable number of data samples for validation. If you specify `-1`, the complete validation set is used for validation. This parameter is deprecated and the number of samples in the validation set is controlled by `--dataset_test_ratio` or `--val_dataset {dataset_name}#{dataset_sample}`.
- `--system`: System used in dialogue template, default is `None`, i.e. use the model's default system. If set to '', no system is used.
- `--tools_prompt`: Select the corresponding tools system prompt for the tools field transformation. The options are ['react_en', 'react_zh', 'toolbench'], which correspond to the English version of ReAct format, Chinese version of ReAct format and the toolbench format, respectively. The default is the English version of the ReAct format. For more information, you can refer to the [Agent Deployment Best Practices](Agent-deployment-best-practices.md).
- `--max_length`: Maximum token length, default is `2048`. Avoids OOM issues caused by individual overly long samples. When `--truncation_strategy delete` is specified, samples exceeding max_length will be deleted. When `--truncation_strategy truncation_left` is specified, the leftmost tokens will be truncated: `input_ids[-max_length:]`. If set to -1, no limit.
- `--truncation_strategy`: Default is `'delete'` which removes sentences exceeding max_length from dataset. `'truncation_left'` will truncate excess text from the left, which may truncate special tokens and affect performance, not recommended.
- `--check_dataset_strategy`: Default is `'none'`, i.e. no checking. If training an LLM model, `'warning'` is recommended as data check strategy. If your training target is sentence classification etc., setting to `'none'` is recommended.

- `--custom_train_dataset_path`: Default value is `[]`. This parameter has been deprecated, please use `--dataset {dataset_path}`.
- `--custom_val_dataset_path`: Default value is `[]`. This parameter is deprecated. Please use `--val_dataset {dataset_path}` instead.
- `--self_cognition_sample`: The number of samples for the self-cognition dataset. Default is `0`. If you set this value to >0, you need to specify `--model_name` and `--model_author` at the same time. This parameter has been deprecated, please use `--dataset self-cognition#{self_cognition_sample}` instead.
- `--model_name`: Default value is `[None, None]`. If self-cognition dataset sampling is enabled (i.e., specifying `--dataset self-cognition` or self_cognition_sample>0), you need to provide two values, representing the Chinese and English names of the model, respectively. For example: `--model_name 小黄 'Xiao Huang'`. If you want to learn more, you can refer to the [Self-Cognition Fine-tuning Best Practices](Self-cognition-best-practice.md).
- `--model_name`: Default is `[None, None]`. If self-cognition dataset sampling is enabled (i.e. self_cognition_sample>0), you need to pass two values, representing the model's Chinese and English names respectively. E.g. `--model_name 小黄 'Xiao Huang'`.
- `--model_author`: Default is `[None, None]`. If self-cognition dataset sampling is enabled, you need to pass two values, representing the author's Chinese and English names respectively. E.g. `--model_author 魔搭 ModelScope`.
- `--quant_method`: Quantization method, default is None. You can choose from 'bnb', 'hqq', 'eetq'.
- `--quantization_bit`: Specifies whether to quantize and number of quantization bits, default is `0`, i.e. no quantization. To use 4bit qlora, set `--sft_type lora --quantization_bit 4`.Hqq support 1,2,3,4,8bit, bnb support 4,8bit
- `--hqq_axis`: Hqq argument. Axis along which grouping is performed. Supported values are 0 or 1. default is `0`
- `--hqq_dynamic_config_path`: Parameters for dynamic configuration. The key is the name tag of the layer and the value is a quantization config. If set, each layer specified by its id will use its dedicated quantization configuration.[ref](https://github.com/mobiusml/hqq?tab=readme-ov-file#custom-quantization-configurations-%EF%B8%8F)
- `--bnb_4bit_comp_dtype`: When doing 4bit quantization, we need to dequantize during model forward and backward passes. This specifies the torch_dtype after dequantization. Default is `'AUTO'`, i.e. consistent with `dtype`. Options: 'fp16', 'bf16', 'fp32'. Has no effect when quantization_bit is 0.
- `--bnb_4bit_quant_type`: Quantization method for 4bit quantization, default is `'nf4'`. Options: 'nf4', 'fp4'. Has no effect when quantization_bit is 0.
- `--bnb_4bit_use_double_quant`: Whether to enable double quantization for 4bit quantization, default is `True`. Has no effect when quantization_bit is 0.
- `--bnb_4bit_quant_storage`: Default vlaue `None`.This sets the storage type to pack the quanitzed 4-bit prarams. Has no effect when quantization_bit is 0.
- `--lora_target_modules`: Specify lora modules, default is `['DEFAULT']`. If lora_target_modules is passed `'DEFAULT'` or `'AUTO'`, look up `lora_target_modules` in `MODEL_MAPPING` based on `model_type` (default specifies qkv). If passed `'ALL'`, all Linear layers (excluding head) will be specified as lora modules. If passed `'EMBEDDING'`, Embedding layer will be specified as lora module. If memory allows, setting to 'ALL' is recommended. You can also set `['ALL', 'EMBEDDING']` to specify all Linear and embedding layers as lora modules. This parameter only takes effect when `sft_type` is 'lora'.
- `--lora_rank`: Default is `8`. Only takes effect when `sft_type` is 'lora'.
- `--lora_alpha`: Default is `32`. Only takes effect when `sft_type` is 'lora'.
- `--lora_dropout_p`: Default is `0.05`, only takes effect when `sft_type` is 'lora'.
- `--init_lora_weights`: Method to initialize LoRA weights, can be specified as `true`, `false`, `gaussian`, `pissa`, or `pissa_niter_[number of iters]`. Default value `true`.
- `--lora_bias_trainable`: Default is `'none'`, options: 'none', 'all'. Set to `'all'` to make all biases trainable.
- `--lora_modules_to_save`: Default is `[]`. If you want to train embedding, lm_head, or layer_norm, you can set this parameter, e.g. `--lora_modules_to_save EMBEDDING LN lm_head`. If passed `'EMBEDDING'`, Embedding layer will be added to `lora_modules_to_save`. If passed `'LN'`, `RMSNorm` and `LayerNorm` will be added to `lora_modules_to_save`.
- `--lora_dtype`: Default is `'AUTO'`, specifies dtype for lora modules. If `AUTO`, follow dtype of original module. Options: 'fp16', 'bf16', 'fp32', 'AUTO'.
- `--use_dora`: Default is `False`, whether to use `DoRA`.
- `--use_rslora`: Default is `False`, whether to use `RS-LoRA`.
- `--neftune_noise_alpha`: The noise coefficient added by `NEFTune` can improve performance of instruction fine-tuning, default is `None`. Usually can be set to 5, 10, 15. See [related paper](https://arxiv.org/abs/2310.05914).
- `--neftune_backend`: The backend of `NEFTune`, default uses `transformers` library, may encounter incompatibility when training VL models, in which case it's recommended to specify as `swift`.
- `--gradient_checkpointing`: Whether to enable gradient checkpointing, default is `True`. This can be used to save memory, although it slightly reduces training speed. Has significant effect when max_length and batch_size are large.
- `--deepspeed`: Specifies the path to the deepspeed configuration file or directly passes in configuration information in json format, default is `None`, i.e. deepspeed is not enabled. Deepspeed can save memory. We have written a default [ZeRO-2 configuration file](https://github.com/modelscope/swift/blob/main/swift/llm/ds_config/zero2.json), [ZeRO-3 configuration file](https://github.com/modelscope/swift/blob/main/swift/llm/ds_config/zero3.json). You only need to specify 'default-zero2' to use the default zero2 config file; specify 'default-zero3' to use the default zero3 config file.
- `--batch_size`: Batch_size during training, default is `1`. Increasing batch_size can improve GPU utilization, but won't necessarily improve training speed, because within a batch, shorter sentences need to be padded to the length of the longest sentence in the batch, introducing invalid computations.
- `--eval_batch_size`: Batch_size during evaluation, default is `None`, i.e. set to 1 when `predict_with_generate` is True, set to `batch_size` when False.
- `--num_train_epochs`: Number of epochs to train, default is `1`. If `max_steps >= 0`, this overrides `num_train_epochs`. Usually set to 3 ~ 5.
- `--max_steps`: Max_steps for training, default is `-1`. If `max_steps >= 0`, this overrides `num_train_epochs`.
- `--optim`: Default is `'adamw_torch'`.
- `--learning_rate`: Default is `None`, i.e. set to 1e-4 if `sft_type` is lora, set to 1e-5 if `sft_type` is full.
- `--weight_decay`: Default is `0.01`.
- `--gradient_accumulation_steps`: Gradient accumulation, default is `None`, set to `math.ceil(16 / self.batch_size / world_size)`. `total_batch_size =  batch_size * gradient_accumulation_steps * world_size`.
- `--max_grad_norm`: Gradient clipping, default is `0.5`.
- `--predict_with_generate`: Whether to use generation for evaluation, default is `False`. If set to False, evaluate using `loss`. If set to True, evaluate using `ROUGE-L` and other metrics. Generative evaluation takes a long time, choose carefully.
- `--lr_scheduler_type`: Default is `'linear'`, options: 'linear', 'cosine', 'constant', etc.
- `--warmup_ratio`: Proportion of warmup in total training steps, default is `0.05`.
- `--eval_steps`: Evaluate every this many steps, default is `50`.
- `--save_steps`: Save every this many steps, default is `None`, i.e. set to `eval_steps`.
- `--save_only_model`: Whether to save only model parameters, without saving intermediate states needed for checkpoint resuming, default is `None`, i.e. if `sft_type` is 'lora' and not using deepspeed (`deepspeed` is `None`), set to False, otherwise set to True (e.g. using full fine-tuning or deepspeed).
- `--save_total_limit`: Number of checkpoints to save, default is `2`, i.e. save best and last checkpoint. If set to -1, save all checkpoints.
- `--logging_steps`: Print training information (e.g. loss, learning_rate, etc.) every this many steps, default is `5`.
- `--dataloader_num_workers`: Default value is `None`. If running on a Windows machine, set it to `0`; otherwise, set it to `1`.
- `--push_to_hub`: Whether to sync push trained checkpoint to ModelScope Hub, default is `False`.
- `--hub_model_id`: Model_id to push to on ModelScope Hub, default is `None`, i.e. set to `f'{model_type}-{sft_type}'`. You can set this to model_id or repo_name. We will infer user_name based on hub_token. If the remote repository to push to does not exist, a new repository will be created, otherwise the previous repository will be reused. This parameter only takes effect when `push_to_hub` is set to True.
- `--hub_token`: SDK token needed for pushing. Can be obtained from [https://modelscope.cn/my/myaccesstoken](https://modelscope.cn/my/myaccesstoken), default is `None`, i.e. obtained from environment variable `MODELSCOPE_API_TOKEN`. This parameter only takes effect when `push_to_hub` is set to True.
- `--hub_private_repo`: Whether to set the permission of the pushed model repository on ModelScope Hub to private, default is `False`. This parameter only takes effect when `push__to_hub` is set to True.
- `--push_hub_strategy`: Push strategy, default is `'push_best'`. Options include: 'end', 'push_best', 'push_last', 'checkpoint', 'all_checkpoints'. 'push_best' means when saving weights each time, push and overwrite the best model from before, 'push_last' means when saving weights each time, push and overwrite the last weights from before, 'end' means only push the best model at the end of training. This parameter only takes effect when `push_to_hub` is set to True.
- `--test_oom_error`: Used to detect whether training will cause OOM, default is `False`. If set to True, will sort the training set in descending order by max_length, easy for OOM testing. This parameter is generally used for testing, use carefully.
- `--disable_tqdm`: Whether to disable tqdm, useful when launching script with `nohup`. Default is `False`, i.e. enable tqdm.
- `--lazy_tokenize`: If set to False, preprocess all text before `trainer.train()`. If set to True, delay encoding text, reducing preprocessing wait and memory usage, useful when processing large datasets. Default is `None`, i.e. we intelligently choose based on template type, usually set to False for LLM models, set to True for multimodal models (to avoid excessive memory usage from loading images and audio).
- `--preprocess_num_proc`: Use multiprocessing when preprocessing dataset (tokenizing text). Default is `1`. Same as `lazy_tokenize` command line argument, used to solve slow preprocessing issue. But this strategy cannot reduce memory usage, so if dataset is huge, `lazy_tokenize` is recommended. Recommended values: 4, 8. Note: When using qwen-audio, this parameter will be forced to 1, because qwen-audio's preprocessing function uses torch's multiprocessing, which will cause compatibility issues.
- `--use_flash_attn`: Whether to use flash attn, default is `None`. Installation steps for flash_attn can be found at [https://github.com/Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention). Models supporting flash_attn can be found in [LLM Supported Models](Supported-models-datasets.md).
- `--ignore_args_error`: Whether to ignore Error thrown by command line parameter errors, default is `False`. Set to True if need to copy code to notebook to run.
- `--check_model_is_latest`: Check if model is latest, default is `True`. Set this to `False` if you need to train offline.
- `--logging_dir`: Default is `None`. I.e. set to `f'{self.output_dir}/runs'`, representing path to store tensorboard files.
- `--report_to`: Default is `['tensorboard']`. You can set `--report_to all` to report to all installed integrations.
- `--acc_strategy`: Default is `'token'`, options include: 'token', 'sentence'.
- `--save_on_each_node`: Takes effect during multi-machine training, default is `True`.
- `--save_strategy`: Strategy for saving checkpoint, default is `'steps'`, options include: 'steps', 'epoch', no'.
- `--evaluation_strategy`: Strategy for evaluation, default is `'steps'`, options include: 'steps', 'epoch', no'.
- `--save_safetensors`: Default is `True`.
- `--include_num_input_tokens_seen`: Default is `False`. Tracks the number of input tokens seen throughout training.
- `--max_new_tokens`: Default is `2048`. This parameter only takes effect when `predict_with_generate` is set to True.
- `--do_sample`: Default is `True`. This parameter only takes effect when `predict_with_generate` is set to True.
- `--temperature`: Default is `0.3`. This parameter only takes effect when `do_sample` is set to True. This parameter will be used as default value in deployment parameters.
- `--top_k`: Default is `20`. This parameter only takes effect when `do_sample` is set to True. This parameter will be used as default value in deployment parameters.
- `--top_p`: Default is `0.7`. This parameter only takes effect when `do_sample` is set to True. This parameter will be used as default value in deployment parameters.
- `--repetition_penalty`: Default is `1.`. This parameter will be used as default value in deployment parameters.
- `--num_beams`: Default is `1`. This parameter only takes effect when `predict_with_generate` is set to True.
- `--gpu_memory_fraction`: Default is `None`. This parameter aims to run training under a specified maximum available GPU memory percentage, used for extreme testing.
- `--train_dataset_mix_ratio`: Default is `0.`. This parameter defines how to mix datasets for training. When this parameter is specified, it will mix the training dataset with a multiple of `train_dataset_mix_ratio` of the general knowledge dataset specified by `train_dataset_mix_ds`. This parameter has been deprecated, please use `--dataset {dataset_name}#{dataset_sample}` to mix datasets.
- `--train_dataset_mix_ds`: Default is `['ms-bench']`. Used for preventing knowledge forgetting, this is the general knowledge dataset. This parameter has been deprecated, please use `--dataset {dataset_name}#{dataset_sample}` to mix datasets.
- `--use_loss_scale`: Default is `False`. When taking effect, strengthens loss weight of some Agent fields (Action/Action Input part) to enhance CoT, has no effect in regular SFT scenarios.
- `--custom_register_path`: Default is `None`. Pass in a `.py` file used to register templates, models, and datasets.
- `--custom_dataset_info`: Default is `None`. Pass in the path to an external `dataset_info.json`, a JSON string, or a dictionary. Used to register custom datasets. The format example: https://github.com/modelscope/swift/blob/main/swift/llm/data/dataset_info.json
- `device_map_config_path`: Manually configure the model's device map from a local file, defaults to None.

### Long Context

- `--rope_scaling`: Default `None`, Support `linear` and `dynamic` to scale positional embeddings. Use when `max_length` exceeds `max_position_embeddings`.

### FSDP Parameters

- `--fsdp`: Default value `''`, the FSDP type, please check [this documentation](https://huggingface.co/docs/transformers/v4.39.3/en/main_classes/trainer#transformers.TrainingArguments.fsdp) for details.

- `--fsdp_config`: Default value `None`, the FSDP config file path.

### Sequence Parallel Parameters

- `--sequence_parallel_size`: Default value `1`, a positive value can be used to split a sequence to multiple GPU to reduce memory usage. The value should divide the GPU count.

### BOFT Parameters

- `--boft_block_size`: BOFT block size, default value is 4.
- `--boft_block_num`: Number of BOFT blocks, cannot be used simultaneously with `boft_block_size`.
- `--boft_target_modules`: BOFT target modules. Default is `['DEFAULT']`. If `boft_target_modules` is set to `'DEFAULT'` or `'AUTO'`, it will look up `boft_target_modules` in the `MODEL_MAPPING` based on `model_type` (default specified as qkv). If set to `'ALL'`, all Linear layers (excluding the head) will be designated as BOFT modules.
- `--boft_dropout`: Dropout value for BOFT, default is 0.0.
- `--boft_modules_to_save`: Additional modules to be trained and saved, default is `None`.

### Vera Parameters

- `--vera_rank`: Size of Vera Attention, default value is 256.
- `--vera_projection_prng_key`: Whether to store the Vera projection matrix, default is True.
- `--vera_target_modules`: Vera target modules. Default is `['DEFAULT']`. If `vera_target_modules` is set to `'DEFAULT'` or `'AUTO'`, it will look up `vera_target_modules` in the `MODEL_MAPPING` based on `model_type` (default specified as qkv). If set to `'ALL'`, all Linear layers (excluding the head) will be designated as Vera modules. Vera modules need to share a same shape.
- `--vera_dropout`: Dropout value for Vera, default is 0.0.
- `--vera_d_initial`: Initial value for Vera's d matrix, default is 0.1.
- `--vera_modules_to_save`: Additional modules to be trained and saved, default is `None`.

### LoRA+ Fine-tuning Parameters

- `--lora_lr_ratio`: Default `None`, recommended value `10~16`, specify this parameter when using lora to enable lora+.

### GaLore Fine-tuning Parameters

- `--use_galore: bool` : Default False, whether to use GaLore.
- `--galore_target_modules: Union[str, List[str]]` : Default None, apply GaLore to attention and mlp when not passed.
- `--galore_rank: int` : Default 128, rank value for GaLore.
- `--galore_update_proj_gap: int` : Default 50, update interval for decomposition matrix.
- `--galore_scale: int` : Default 1.0, matrix weight coefficient.
- `--galore_proj_type: str` : Default `std`, GaLore matrix decomposition type.
- `--galore_optim_per_parameter: bool` : Default False, whether to set a separate optimizer for each Galore target Parameter.
- `--galore_with_embedding: bool` : Default False, whether to apply GaLore to embedding.

### LISA Fine-tuning Parameters

Note: LISA only supports full training, which is `--sft_type full`.

- `--lisa_activated_layers`: Default value`0`, which means use without `LISA`, suggested value is `2` or `8`.
- `--lisa_step_interval`: Default value `20`, how many iters to switch the layers to back-propagate.

### UNSLOTH Fine-tuning Parameters

unsloth has no new parameters，you can use the existing parameters to use unsloth:

```
--tuner_backend unsloth
--sft_type full/lora
--quantization_type 4
```

### LLaMA-PRO Fine-tuning Parameters

- `--llamapro_num_new_blocks`: Default `4`, total number of new layers inserted.
- `--llamapro_num_groups`: Default `None`, how many groups to insert new_blocks into, if `None` then equals `llamapro_num_new_blocks`, i.e. each new layer is inserted into original model separately.

### AdaLoRA Fine-tuning Parameters

The following parameters take effect when `sft_type` is set to `adalora`. AdaLoRA's `target_modules` and other parameters inherit from lora's corresponding parameters, but the `lora_dtype` parameter has no effect.

- `--adalora_target_r`: Default `8`, AdaLoRA's average rank.
- `--adalora_init_r`: Default `12`, AdaLoRA's initial rank.
- `--adalora_tinit`: Default `0`, AdaLoRA's initial warmup.
- `--adalora_tfinal`: Default `0`, AdaLoRA's final warmup.
- `--adalora_deltaT`: Default `1`, AdaLoRA's step interval.
- `--adalora_beta1`: Default `0.85`, AdaLoRA's EMA parameter.
- `--adalora_beta2`: Default `0.85`, AdaLoRA's EMA parameter.
- `--adalora_orth_reg_weight`: Default `0.5`, AdaLoRA's regularization parameter.

### IA3 Fine-tuning Parameters

The following parameters take effect when `sft_type` is set to `ia3`.

- `--ia3_target_modules`: Specify IA3 target modules, default is `['DEFAULT']`. See `lora_target_modules` for specific meaning.
- `--ia3_feedforward_modules`: Specify the Linear name of IA3's MLP, this name must be in `ia3_target_modules`.
- `--ia3_modules_to_save`: Additional modules participating in IA3 training. See meaning of `lora_modules_to_save`.

## dpo Parameters

dpo parameters inherit from sft parameters, with the following added parameters:

- `--ref_model_type`: Type of reference model, available `model_type` options can be found in `MODEL_MAPPING.keys()`.
- `--ref_model_id_or_path`: The local cache dir for reference model, default `None`.
- `--max_prompt_length`: Maximum prompt length, this parameter is passed to DPOTrainer, setting prompt length to not exceed this value, default is `1024`.
- `--beta`: Regularization term for DPO logits, default is 0.1.
- `--label_smoothing`: Whether to use DPO smoothing, default is 0, generally set between 0~0.5.
- `--loss_type`: DPOloss type, supports 'sigmoid', 'hinge', 'ipo', 'kto_pair', default is 'sigmoid'.
- `--sft_beta`: Whether to add sft loss in DPO, default is 0.1, supports [0, 1) interval, final loss is `(1-sft_beta)*KL_loss + sft_beta * sft_loss`.

## merge-lora infer Parameters

- `--model_type`: Default is `None`, see `sft.sh command line arguments` for parameter details.
- `--model_id_or_path`: Default is `None`, see `sft.sh command line arguments` for parameter details. Recommended to use model_type to specify.
- `--model_revision`: Default is `None`. See `sft.sh command line arguments` for parameter details. If `model_id_or_path` is None or a local model directory, this parameter has no effect.
- `--sft_type`: Default is `'lora'`, see `sft.sh command line arguments` for parameter details.
- `--template_type`: Default is `'AUTO'`, see `sft.sh command line arguments` for parameter details.
- `--infer_backend`: Options are 'AUTO', 'vllm', 'pt'. Default uses 'AUTO', for intelligent selection, i.e. if `ckpt_dir` is not passed or using full fine-tuning, and vllm is installed and model supports vllm, then use vllm engine, otherwise use native torch for inference. vllm environment setup can be found in [VLLM Inference Acceleration and Deployment](VLLM-inference-acceleration-and-deployment.md), vllm supported models can be found in [Supported Models](Supported-models-datasets.md).
- `--ckpt_dir`: Required, value is the checkpoint path saved in SFT stage, e.g. `'/path/to/your/vx-xxx/checkpoint-xxx'`.
- `--load_args_from_ckpt_dir`: Whether to read model configuration info from `sft_args.json` file in `ckpt_dir`. Default is `True`.
- `--load_dataset_config`: This parameter only takes effect when `--load_args_from_ckpt_dir true`. I.e. whether to read dataset related configuration from `sft_args.json` file in `ckpt_dir`. Default is `False`.
- `--eval_human`: Whether to evaluate using validation set portion of dataset or manual evaluation. Default is `None`, for intelligent selection, if no datasets (including custom datasets) are passed, manual evaluation will be used. If datasets are passed, dataset evaluation will be used.
- `device_map_config_path`: Manually configure the model's device map from a local file, defaults to None.
- `--seed`: Default is `42`, see `sft.sh command line arguments` for parameter details.
- `--dtype`: Default is `'AUTO`, see `sft.sh command line arguments` for parameter details.
- `--dataset`: Default is `[]`, see `sft.sh command line arguments` for parameter details.
- `--val_dataset`: Default is `[]`, see `sft.sh command line arguments` for parameter details.
- `--dataset_seed`: Default is `42`, see `sft.sh command line arguments` for parameter details.
`--dataset_test_ratio`: Default value is `0.01`. For specific parameter details, refer to the `sft.sh command line arguments`.
- `--show_dataset_sample`: Represents number of validation set samples to evaluate and display, default is `10`.
- `--system`: Default is `None`. See `sft.sh command line arguments` for parameter details.
- `--tools_prompt`: Default is `react_en`. See `sft.sh command line arguments` for parameter details.
- `--max_length`: Default is `-1`. See `sft.sh command line arguments` for parameter details.
- `--truncation_strategy`: Default is `'delete'`. See `sft.sh command line arguments` for parameter details.
- `--check_dataset_strategy`: Default is `'none'`, see `sft.sh command line arguments` for parameter details.
- `--custom_train_dataset_path`: Default value is `[]`. This parameter has been deprecated, please use `--dataset {dataset_path}`.
- `--custom_val_dataset_path`: Default value is `[]`. This parameter is deprecated. Please use `--val_dataset {dataset_path}` instead.
- `--quantization_bit`: Default is 0. See `sft.sh command line arguments` for parameter details.
- `--quant_method`: Quantization method, default is None. You can choose from 'bnb', 'hqq', 'eetq'.
- `--hqq_axis`: Hqq argument. Axis along which grouping is performed. Supported values are 0 or 1. default is `0`
- `--hqq_dynamic_config_path`: Parameters for dynamic configuration. The key is the name tag of the layer and the value is a quantization config. If set, each layer specified by its id will use its dedicated quantization configuration.[ref](https://github.com/mobiusml/hqq?tab=readme-ov-file#custom-quantization-configurations-%EF%B8%8F)
- `--bnb_4bit_comp_dtype`: Default is `'AUTO'`.  See `sft.sh command line arguments` for parameter details. If `quantization_bit` is set to 0, this parameter has no effect.
- `--bnb_4bit_quant_type`: Default is `'nf4'`.  See `sft.sh command line arguments` for parameter details. If `quantization_bit` is set to 0, this parameter has no effect.
- `--bnb_4bit_use_double_quant`: Default is `True`.  See `sft.sh command line arguments` for parameter details. If `quantization_bit` is set to 0, this parameter has no effect.
- `--bnb_4bit_quant_storage`: Default value `None`.See `sft.sh command line arguments` for parameter details. If `quantization_bit` is set to 0, this parameter has no effect.
- `--max_new_tokens`: Maximum number of new tokens to generate, default is `2048`.
- `--do_sample`: Whether to use greedy generation or sampling generation, default is `True`.
- `--temperature`: Default is `0.3`. This parameter only takes effect when `do_sample` is set to True. This parameter will be used as default value in deployment parameters.
- `--top_k`: Default is `20`. This parameter only takes effect when `do_sample` is set to True. This parameter will be used as default value in deployment parameters.
- `--top_p`: Default is `0.7`. This parameter only takes effect when `do_sample` is set to True. This parameter will be used as default value in deployment parameters.
- `--repetition_penalty`: Default is `1.`. This parameter will be used as default value in deployment parameters.
- `--num_beams`: Default is `1`.
- `--use_flash_attn`: Default is `None`, i.e. 'auto'. See `sft.sh command line arguments` for parameter details.
- `--ignore_args_error`: Default is `False`, see `sft.sh command line arguments` for parameter details.
- `--stream`: Whether to use streaming output, default is `True`. This parameter only takes effect when using dataset evaluation and verbose is True.
- `--merge_lora`: Whether to merge lora weights into base model and save full weights, default is `False`. Weights will be saved in the same level directory as `ckpt_dir`, e.g. `'/path/to/your/vx-xxx/checkpoint-xxx-merged'` directory.
- `--merge_device_map`: device_map used when merge-lora, default is `None`, to reduce memory usage, use `auto` only during merge-lora process, otherwise default is `cpu`.
- `--save_safetensors`: Whether to save as `safetensors` file or `bin` file. Default is `True`.
- `--overwrite_generation_config`: Whether to save the generation_config used for evaluation as `generation_config.json` file, default is `None`. If `ckpt_dir` is specified, set to `True`, otherwise set to `False`. The generation_config file saved during training will be overwritten.
- `--verbose`: If set to False, use tqdm style inference. If set to True, output inference query, response, label. Default is `None`, for auto selection, i.e. when `len(val_dataset) >= 100`, set to False, otherwise set to True. This parameter only takes effect when using dataset evaluation.
- `--gpu_memory_utilization`: Parameter for initializing vllm engine `EngineArgs`, default is `0.9`. This parameter only takes effect when using vllm. VLLM inference acceleration and deployment can be found in [VLLM Inference Acceleration and Deployment](VLLM-inference-acceleration-and-deployment.md).
- `--tensor_parallel_size`: Parameter for initializing vllm engine `EngineArgs`, default is `1`. This parameter only takes effect when using vllm.
- `--max_model_len`: Override model's max_model__len, default is `None`. This parameter only takes effect when using vllm.
- `--vllm_enable_lora`: Default `False`. Whether to support vllm with lora.
- `--vllm_max_lora_rank`: Default `16`.  Lora rank in VLLM.
- `--lora_modules`: Default`[]`, the input format is `'{lora_name}={lora_path}'`, e.g. `--lora_modules lora_name1=lora_path1 lora_name2=lora_path2`. `ckpt_dir` will be added with `f'default-lora={args.ckpt_dir}'` by default.
- `--custom_register_path`: Default is `None`. Pass in a `.py` file used to register templates, models, and datasets.
- `--custom_dataset_info`: Default is `None`. Pass in the path to an external `dataset_info.json`, a JSON string, or a dictionary. Used for expanding datasets.
- `--rope_scaling`: Default `None`, Support `linear` and `dynamic` to scale positional embeddings. Use when `max_length` exceeds `max_position_embeddings`.


## export Parameters

export parameters inherit from infer parameters, with the following added parameters:

- `--merge_lora`: Default is `False`. This parameter is already defined in InferArguments, not a new parameter. Whether to merge lora weights into base model and save full weights. Weights will be saved in the same level directory as `ckpt_dir`, e.g. `'/path/to/your/vx-xxx/checkpoint-xxx-merged'` directory.
- `--quant_bits`: Number of bits for quantization. Default is `0`, i.e. no quantization. If you set `--quant_method awq`, you can set this to `4` for 4bits quantization. If you set `--quant_method gptq`, you can set this to `2`,`3`,`4`,`8` for corresponding bits quantization. If quantizing original model, weights will be saved in `f'{args.model_type}-{args.quant_method}-int{args.quant_bits}'` directory. If quantizing fine-tuned model, weights will be saved in the same level directory as `ckpt_dir`, e.g. `f'/path/to/your/vx-xxx/checkpoint-xxx-{args.quant_method}-int{args.quant_bits}'` directory.
- `--quant_method`: Quantization method, default is `'awq'`. Options are 'awq', 'gptq'.
- `--dataset`: This parameter is already defined in InferArguments, for export it means quantization dataset. Default is `[]`. More details: including how to customize quantization dataset, can be found in [LLM Quantization Documentation](LLM-quantization.md).
- `--quant_n_samples`: Quantization parameter, default is `256`. When set to `--quant_method awq`, if OOM occurs during quantization, you can moderately reduce `--quant_n_samples` and `--quant_seqlen`. `--quant_method gptq` generally does not encounter quantization OOM.
- `--quant_seqlen`: Quantization parameter, default is `2048`.
- `--quant_device_map`: Default is `'cpu'`, to save memory. You can specify 'cuda:0', 'auto', 'cpu', etc., representing the device to load model during quantization. This parameter is independent of the actual device that performs the quantization, such as AWQ and GPTQ which will carry out quantization on cuda:0.
- `quant_output_dir`: Default is `None`, the default quant_output_dir will be printed in the command line.
- `--push_to_hub`: Default is `False`. Whether to push the final `ckpt_dir` to ModelScope Hub. If you specify `merge_lora`, full parameters will be pushed; if you also specify `quant_bits`, quantized model will be pushed.
- `--hub_model_id`: Default is `None`. Model_id to push to on ModelScope Hub. If `push_to_hub` is set to True, this parameter must be set.
- `--hub_token`: Default is `None`. See `sft.sh command line arguments` for parameter details.
- `--hub_private_repo`: Default is `False`. See `sft.sh command line arguments` for parameter details.
- `--commit_message`: Default is `'update files'`.

## eval parameters

The eval parameters inherit from the infer parameters, and additionally include the following parameters: (Note: The generation_config parameter in infer will be invalid, controlled by [evalscope](https://github.com/modelscope/eval-scope).)

- `--eval_dataset`: The official dataset for evaluation, with a default value of `['ceval', 'gsm8k', 'arc']`. Possible values include: 'arc', 'gsm8k', 'mmlu', 'cmmlu', 'ceval', 'bbh', 'general_qa'. If only custom datasets need to be evaluated, this parameter can be set to `no`.
- `--eval_few_shot`: The few-shot number of sub-datasets for each evaluation set, with a default value of `None`, meaning to use the default configuration of the dataset.
- `--eval_limit`: The sampling quantity for each sub-dataset of the evaluation set, with a default value of `None` indicating full-scale evaluation.
- `--name`: Used to differentiate the result storage path for evaluating the same configuration, with the current time as the default.
- `--eval_url`: The standard model invocation interface for OpenAI, for example, `http://127.0.0.1:8000/v1`. This needs to be set when evaluating in a deployed manner, usually not needed. Default is `None`.
  ```shell
  swift eval --eval_url http://127.0.0.1:8000/v1 --eval_is_chat_model true --model_type gpt4 --eval_token xxx
  ```
- `--eval_token`: The token for the standard model invocation interface for OpenAI, with a default value of `'EMPTY'`, indicating no token.
- `--eval_is_chat_model`: If `eval_url` is not empty, this value needs to be passed to determine if it is a "chat" model. False represents a "base" model. Default is `None`.
- `--custom_eval_config`: Used for evaluating with custom datasets, and needs to be a locally existing file path. For details on file format, refer to [Custom Evaluation Set](./LLM-eval.md#Custom-Evaluation-Set). Default is `None`.
- `--eval_use_cache`: Whether to use already generated evaluation cache, so that previously evaluated results won't be rerun but only the evaluation results regenerated. Default is `False`.


## app-ui Parameters

app-ui parameters inherit from infer parameters, with the following added parameters:

- `--host`: Default is `'127.0.0.1'`. Passed to the `demo.queue().launch(...)` function of gradio.
- `--port`: Default is `7860`. Passed to the `demo.queue().launch(...)` function of gradio.
- `--share`: Default is `False`. Passed to the `demo.queue().launch(...)` function of gradio.

## deploy Parameters

deploy parameters inherit from infer parameters, with the following added parameters:

- `--host`: Default is `'127.0.0.1`.
- `--port`: Default is `8000`.
- `--ssl_keyfile`: Default is `None`.
- `--ssl_certfile`: Default is `None`.