base_model: meta-llama/Llama-3.1-8B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

datasets:
  - path: togethercomputer/Long-Data-Collections
    type: completion
    field: text
    data_files:
      - pretrain/rp_sub.jsonl.zst
  - path: princeton-nlp/TextbookChapters
    type: completion
    field: chapter
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out

sequence_len: 500_000
min_sample_len: 200_000
sample_packing: true

tiled_mlp: true
context_parallel_size: 8
plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch_8bit
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: true

gradient_checkpointing: true
activation_offloading: legacy

resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_steps: 100
saves_per_epoch: 1
evals_per_epoch: 2
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>

fsdp_version: 2
fsdp_config:
  offload_params: false  # offloading is currently not compatible with SP + torchao optimizer
  state_dict_type: SHARDED_STATE_DICT
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: LlamaDecoderLayer
  reshard_after_forward: true

# save_first_step: true  # uncomment this to validate checkpoint saving works with your config