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# The name of the model to pretrain. Choose from names in ``litgpt.config``. Mutually exclusive with
# ``model_config``. (type: Optional[str], default: null)
model_name: "tiny-llama-1.1b"
# A ``litgpt.Config`` object to define the model architecture. Mutually exclusive with
# ``model_config``. (type: Optional[Config], default: null)
model_config:
padded_vocab_size: 32768
vocab_size: 32768
block_size: 32768
n_layer: 10
n_head: 12
head_size: null
n_embd: 312
n_query_groups: 4
rotary_percentage: 1.0
parallel_residual: false
bias: false
norm_class_name: "RMSNorm"
norm_eps: 1e-05
mlp_class_name: "LLaMAMLP"
intermediate_size: 1092
rope_base: 500000
# Directory in which to save checkpoints and logs. If running in a Lightning Studio Job, look for it in
# /teamspace/jobs/<job-name>/share. (type: <class 'Path'>, default: out/pretrain)
out_dir: "../out/contrain/"
# The precision to use for pretraining. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)
# precision: bf16-mixed
precision: bf16-true
# Optional path to a checkpoint directory to initialize the model from.
# Useful for continued pretraining. Mutually exclusive with ``resume``. (type: Optional[Path], default: null)
initial_checkpoint_dir: "../out/pretrain/pretrained_checkpoint_converted"
# Path to a checkpoint directory to resume from in case training was interrupted, or ``True`` to resume
# from the latest checkpoint in ``out_dir``. An error will be raised if no checkpoint is found. Passing
# ``'auto'`` will resume from the latest checkpoint but not error if no checkpoint exists.
# (type: Union[bool, Literal["auto"], Path], default: False)
resume: false
# resume: "auto"
# Data-related arguments. If not provided, the default is ``litgpt.data.TinyLlama``.
data:
class_path: LitData
init_args:
data_path: "../data/"
num_workers: 16
# Training-related arguments. See ``litgpt.args.TrainArgs`` for details
train:
# Number of optimizer steps between saving checkpoints (type: Optional[int], default: 1000)
save_interval: 100
# Number of iterations between logging calls (type: int, default: 1)
log_interval: 1
# Number of samples between optimizer steps across data-parallel ranks (type: int, default: 512)
# global_batch_size: 512
global_batch_size: 128
# Number of samples per data-parallel rank (type: int, default: 4)
micro_batch_size: 1
# micro_batch_size: 16
# micro_batch_size: 14
# Number of iterations with learning rate warmup active (type: int, default: 2000)
lr_warmup_steps: 2000
# Number of epochs to train on (type: Optional[int], default: null)
epochs:
# Total number of tokens to train on (type: Optional[int], default: 3000000000000)
# max_tokens: 3000000000000
max_tokens: 4252334823 # 129767 * 32769 * 1
# Limits the number of optimizer steps to run. (type: Optional[int], default: null)
max_steps:
# Limits the length of samples. Off by default (type: Optional[int], default: null)
max_seq_length: 32768
# Whether to tie the embedding weights with the language modeling head weights. (type: Optional[bool], default: False)
tie_embeddings:
# (type: Optional[float], default: 1.0)
max_norm: 1.0
# (type: float, default: 4e-05)
min_lr: 4.0e-05
# Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for details
eval:
# Number of optimizer steps between evaluation calls (type: int, default: 1000)
interval: 100
# Number of tokens to generate (type: Optional[int], default: null)
max_new_tokens:
# Number of iterations (type: int, default: 100)
max_iters: 100
# Whether to evaluate on the validation set at the beginning of the training
initial_validation: false
# Whether to evaluate on the validation set at the end the training
final_validation: true
# Optimizer-related arguments
optimizer:
# class_path: torch.optim.AdamW
class_path: grokadamw.GrokAdamW
# class_path: bitsandbytes.optim.AdamW8bit
# class_path: bitsandbytes.optim.PagedAdamW8bit
init_args:
# (type: float, default: 0.001)
lr: 1.0e-4
# (type: float, default: 0.01)
weight_decay: 0.01
# (type: tuple, default: (0.9,0.999))
betas:
- 0.9
- 0.95
# How many devices/GPUs to use. Uses all GPUs by default. (type: Union[int, str], default: auto)
devices: auto
# How many nodes to use. (type: int, default: 1)
num_nodes: 1
# Optional path to the tokenizer dir that was used for preprocessing the dataset. Only some data
# module require this. (type: Optional[Path], default: null)
tokenizer_dir: "../"
# The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: tensorboard)
logger_name: "wandb"
# The random seed to use for reproducibility. (type: int, default: 42)
seed: 42
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