base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
# optionally might have model_type or tokenizer_type
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

max_steps: 200
pretraining_dataset:
  - path: allenai/c4
    name: en
    type: pretrain
dataset_prepared_path:
val_set_size: 0.0
output_dir: ./outputs/model-out

sequence_len: 2048
sample_packing: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
tf32: false

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_steps: 10
evals_per_epoch:
saves_per_epoch: 1
weight_decay: 0.0
special_tokens: