example-local-jsonl.yaml

###############################################
##
## Trainer settings are kept minimal
## with checkpoint / model saving disabled,
## as this tutorial focuses only on datasets
## 
## It only runs 10 steps, enough to prove that
## the dataset configs are valid
##
## See the full `config-example.yaml` for more
## detailes on the trainer/model configs
##
###############################################

trainer:
  max_steps: 10
  # Resonable batch size, for a more realistic it/s rate
  target_batch_size: 32

model:
  load_model: ../model/L6-D512-world-init.pth
  ctx_len: 1024
  lr_init: 3e-4

  # BPTT learning, this allows you to run the trainer against dataset
  # larger then its training context length
  bptt_learning: true
  bptt_learning_range: -1

########################################
## Training model settings
########################################
data:
  # dataset_path for the prebuilt dataset, using HF `load_from_disk()`
  #
  # Use this if you have built your own dataset and saved it with `save_to_disk()`
  # with source left as null. Other wise configure this to a directory which the 
  # dataset will be built and tokenized by the huggingface dataset process.
  data_path: ../datapath/sample-mem-train/

  # Other wise provide the source path, which is used as huggingface dataset path
  # this will be used to populate the dataset_path
  #
  # Use either the following
  # - hugging face dataset 
  # - Directory path to a directory containing dataset files
  # - Path to a single dataset file
  # - hugging face dataset mode (ie: text,csv,etc - use data_dir, to configure the path then)
  # - null
  #
  # If source is disabled, all other params, except data_path, is ignored
  source: "json"
  
  # Use data_dir, if you are using source=text/json/etc
  # this should be relative to the trainer script path
  source_data_dir: "../dataset/dataset-config/jsonl/"

  # Tokenizer to use, use either the inbuilt 'neox', or 'neox' tokenizer
  # If using a custom tokenizer, provide the tokenizer file path
  # ---
  tokenizer: world

  # After loading the dataset, split out test data used for validation, 
  # This process is skipped if the dataset includes a test split
  # This process is skipped if set to zero
  test_split: 0.01
  test_split_shuffle: false