output_dir: "" # just a placeholder
bf16: true
model_name_or_path: meta-llama/Llama-3.2-1B-Instruct
label_names: ["labels"]
# eval_on_start: True
# eval_strategy: "steps"
# eval_steps: 500
# save_strategy: "no"
# # save_steps: 500
# logging_strategy: "steps"
# logging_steps: 100
# use_liger_kernel: true
# remove_unused_columns: false

# needed to avoid OOM by compute the metrics batch by batch
# w/o this the trainer stores logits of all sample in memory...
# batch_eval_metrics: true

per_device_train_batch_size: 64
per_device_eval_batch_size: 128
max_new_tokens: 64
gen_per_device_eval_batch_size: 128
max_val_samples_per_ds: 500
# optim: schedule_free_adamw
learning_rate: 0.0001
# lr_scheduler_type: "constant_with_warmup"
neftune_noise_alpha: 1
weight_decay: 0.1
warmup_ratio: 0.05

# LoRA
lora_r: 16
lora_dropout: 0.05
target_modules:
  - down_proj
  - up_proj
  - gate_proj

# data
train_ds_names:
- data/raw_datasets/context_numbers_2
- data/raw_datasets/context_numbers_3
- data/raw_datasets/context_numbers_4
- data/raw_datasets/context_numbers_5
- data/raw_datasets/context_numbers_6
- data/raw_datasets/context_numbers_7
- data/raw_datasets/context_numbers_8
- data/raw_datasets/context_numbers_9
- data/raw_datasets/context_numbers_10

val_ds_names:
- data/raw_datasets/context_numbers_2
- data/raw_datasets/context_numbers_3
- data/raw_datasets/context_numbers_4
- data/raw_datasets/context_numbers_5
- data/raw_datasets/context_numbers_6
- data/raw_datasets/context_numbers_7
- data/raw_datasets/context_numbers_8
- data/raw_datasets/context_numbers_9
- data/raw_datasets/context_numbers_10

test_ds_names:
- data/raw_datasets/context_numbers_11
- data/raw_datasets/context_numbers_12
- data/raw_datasets/context_numbers_13
- data/raw_datasets/context_numbers_14
- data/raw_datasets/context_numbers_15