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: 8
per_device_eval_batch_size: 8
max_val_samples_per_ds: 1000
# optim: schedule_free_adamw

learning_rate: 0.00002
# lr_scheduler_type: "constant_with_warmup"
neftune_noise_alpha: 5
weight_decay: 0.01
# warmup_ratio: 0.1
warmup_steps: 100

dataloader_prefetch_factor: 8
dataloader_num_workers: 8
# LoRA
lora_r: 8
lora_dropout: 0.05
target_modules:
  - down_proj
  - up_proj

# data
train_ds_names:
- openmathintx-2
- opencoder-edu

val_ds_names:
- gsm8k
- opencoder-edu

load_best_model_at_end: true
metric_for_best_model: eval_gsm8k_loss