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: 128
per_device_eval_batch_size: 128
# optim: schedule_free_adamw
learning_rate: 0.00001
# lr_scheduler_type: "constant_with_warmup"
neftune_noise_alpha: 1
weight_decay: 0.01
warmup_ratio: 0.1

# 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

val_ds_names:
- data/raw_datasets/context_numbers_2

test_ds_names:
- data/raw_datasets/context_numbers_2