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microllama_v2.yaml

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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: micro-llama-300M-v2
+
+# A ``litgpt.Config`` object to define the model architecture. Mutually exclusive with
+# ``model_config``. (type: Optional[Config], default: null)
+model_config:
+
+# 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/pretrain/micro-llama-v2
+
+# The precision to use for pretraining. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)
+precision: bf16-mixed
+
+# 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: /root/litgpt/out_lightning_ai/pretrain/micro-llama-v2/step-00128000/
+initial_checkpoint_dir: /root/litgpt/out_lightning_ai/step-00128000-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
+
+# Data-related arguments. If not provided, the default is ``litgpt.data.TinyLlama``.
+data: MicroLlama
+
+# Training-related arguments. See ``litgpt.args.TrainArgs`` for details
+train:
+
+  # Number of optimizer steps between saving checkpoints (type: Optional[int], default: 1000)
+  save_interval: 1000
+
+  # Number of iterations between logging calls (type: int, default: 1)
+  log_interval: 10
+
+  # Number of samples between optimizer steps across data-parallel ranks (type: int, default: 48)
+  # Scale this number according to the number of GPU and memory size per GPU
+  # For example, we used 16 for 4 x 48G L40s 
+  global_batch_size: 32
+
+  # Number of samples per data-parallel rank (type: int, default: 12)
+  # Scale this number according to the memory size per GPU
+  # For example, we used 12 for 24G 4090
+  micro_batch_size: 4
+
+  # 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
+
+  # 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: 2048
+
+  # 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: 1000
+
+  # 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
+
+# Optimizer-related arguments
+optimizer:
+
+  class_path: torch.optim.AdamW
+  
+  init_args:
+    
+    #   (type: float, default: 0.001)
+    lr: 4e-4
+    
+    #   (type: float, default: 0.01)
+    weight_decay: 0.1
+    
+    #   (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: checkpoints/meta-llama/Llama-3.2-1B
+
+# 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