base_model: meta-llama/Llama-3.2-3B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: ptllama/acemath_test
    type: completion

# pretraining_dataset:
#   - name:
#     path: ptllama/acemath_test
#     split:
#     text_column: text # column in dataset with the data, usually `text`
#     type: pretrain
#     trust_remote_code:
#     skip: # number of rows of data to skip over from the beginning

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: axolotl-pretraining
wandb_entity:
wandb_watch:
wandb_name: test-2e4
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 4
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-4

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.01
cosine_min_lr_ratio: 0.1
cosine_constant_lr_ratio: 0.9
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>