output_dir: "" # just a placeholder
bf16: true
model_name_or_path: meta-llama/Llama-3.2-1B-Instruct
label_names: ["labels"]
add_repeat_prompt: false
add_negative_prompt: false

# 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: 32
per_device_eval_batch_size: 1
max_val_samples_per_ds: 20
# optim: schedule_free_adamw
learning_rate: 0.00001
# lr_scheduler_type: "constant_with_warmup"
neftune_noise_alpha: 1
weight_decay: 0.01


# 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_4
- data/raw_datasets/context_numbers_8
- data/raw_datasets/context_numbers_16
- data/raw_datasets/context_numbers_24
- data/raw_datasets/context_numbers_32
- data/raw_datasets/context_numbers_40
- data/raw_datasets/context_numbers_48
- data/raw_datasets/context_numbers_56
- data/raw_datasets/context_numbers_64

val_ds_names:
- data/raw_datasets/context_numbers_16
- data/raw_datasets/context_numbers_32
- data/raw_datasets/context_numbers_64
- data/raw_datasets/context_numbers_128
- data/raw_datasets/context_numbers_256

test_ds_names:
- data/raw_datasets/context_numbers_16
- data/raw_datasets/context_numbers_32
- data/raw_datasets/context_numbers_64
- data/raw_datasets/context_numbers_128
- data/raw_datasets/context_numbers_256