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| # The path to the base model's checkpoint directory to load for finetuning. (type: <class 'Path'>, default: checkpoints/stabilityai/stablelm-base-alpha-3b) | ||
| checkpoint_dir: checkpoints/google/gemma-7b | ||
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| # Directory in which to save checkpoints and logs. (type: <class 'Path'>, default: out/lora) | ||
| out_dir: out/finetune/qlora-gemma-7b | ||
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| # The precision to use for finetuning. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null) | ||
| precision: bf16-true | ||
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| # If set, quantize the model with this algorithm. See ``tutorials/quantize.md`` for more information. (type: Optional[Literal['nf4', 'nf4-dq', 'fp4', 'fp4-dq', 'int8-training']], default: null) | ||
| quantize: | ||
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| # How many devices/GPUs to use. (type: Union[int, str], default: 1) | ||
| devices: 1 | ||
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| # The LoRA rank. (type: int, default: 8) | ||
| lora_r: 16 | ||
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| # The LoRA alpha. (type: int, default: 16) | ||
| lora_alpha: 16 | ||
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| # The LoRA dropout value. (type: float, default: 0.05) | ||
| lora_dropout: 0.1 | ||
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| # Whether to apply LoRA to the query weights in attention. (type: bool, default: True) | ||
| lora_query: true | ||
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| # Whether to apply LoRA to the key weights in attention. (type: bool, default: False) | ||
| lora_key: true | ||
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| # Whether to apply LoRA to the value weights in attention. (type: bool, default: True) | ||
| lora_value: true | ||
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| # Whether to apply LoRA to the output projection in the attention block. (type: bool, default: False) | ||
| lora_projection: true | ||
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| # Whether to apply LoRA to the weights of the MLP in the attention block. (type: bool, default: False) | ||
| lora_mlp: true | ||
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| # Whether to apply LoRA to output head in GPT. (type: bool, default: False) | ||
| lora_head: true | ||
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| # Data-related arguments. If not provided, the default is ``litgpt.data.Alpaca``. | ||
| data: | ||
| class_path: litgpt.data.Alpaca2k | ||
| init_args: | ||
| mask_prompt: false | ||
| val_split_fraction: 0.03847 | ||
| prompt_style: alpaca | ||
| ignore_index: -100 | ||
| seed: 42 | ||
| num_workers: 4 | ||
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| # Training-related arguments. See ``litgpt.args.TrainArgs`` for details | ||
| train: | ||
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| # Number of optimizer steps between saving checkpoints (type: Optional[int], default: 1000) | ||
| save_interval: 800 | ||
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| # Number of iterations between logging calls (type: int, default: 1) | ||
| log_interval: 1 | ||
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| # Number of samples between optimizer steps across data-parallel ranks (type: int, default: 128) | ||
| global_batch_size: 6 | ||
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| # Number of samples per data-parallel rank (type: int, default: 4) | ||
| micro_batch_size: 1 | ||
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| # Number of iterations with learning rate warmup active (type: int, default: 100) | ||
| lr_warmup_steps: 200 | ||
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| # Number of epochs to train on (type: Optional[int], default: 5) | ||
| epochs: 2 | ||
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| # Total number of tokens to train on (type: Optional[int], default: null) | ||
| max_tokens: | ||
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| # Limits the number of optimizer steps to run. (type: Optional[int], default: null) | ||
| max_steps: | ||
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| # Limits the length of samples. Off by default (type: Optional[int], default: null) | ||
| max_seq_length: 512 | ||
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| # Whether to tie the embedding weights with the language modeling head weights. (type: Optional[bool], default: null) | ||
| tie_embeddings: | ||
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| # (type: float, default: 0.0003) | ||
| learning_rate: 0.0002 | ||
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| # (type: float, default: 0.02) | ||
| weight_decay: 0.0 | ||
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| # (type: float, default: 0.9) | ||
| beta1: 0.9 | ||
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| # (type: float, default: 0.95) | ||
| beta2: 0.95 | ||
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| # (type: Optional[float], default: null) | ||
| max_norm: | ||
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| # (type: float, default: 6e-05) | ||
| min_lr: 6.0e-05 | ||
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| # Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for details | ||
| eval: | ||
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| # Number of optimizer steps between evaluation calls (type: int, default: 100) | ||
| interval: 25 | ||
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| # Number of tokens to generate (type: Optional[int], default: 100) | ||
| max_new_tokens: 100 | ||
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| # Number of iterations (type: int, default: 100) | ||
| max_iters: 100 | ||
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| # The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: csv) | ||
| logger_name: csv | ||
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| # The random seed to use for reproducibility. (type: int, default: 1337) | ||
| seed: 1337 |
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