doc-to-lora/configs/fw_qa_pretrain_mini.yaml

46 lines
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YAML

output_dir: "" # just a placeholder
bf16: true
model_name_or_path: google/gemma-2-2b-it
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: 0.00004
# lr_scheduler_type: "constant_with_warmup"
neftune_noise_alpha: 5
weight_decay: 0.01
#
warmup_steps: 100
dataloader_prefetch_factor: 8
dataloader_num_workers: 8
# LoRA
lora_r: 8
lora_dropout: 0.0
target_modules:
- down_proj
# data
train_ds_names:
- fw_qa_3_mini_pretrain
val_ds_names:
- fw_qa_3_pretrain
load_best_model_at_end: true
metric_for_best_model: eval_fw_qa_3_pretrain_loss