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53 lines
1.3 KiB
YAML
53 lines
1.3 KiB
YAML
output_dir: "" # just a placeholder
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bf16: true
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model_name_or_path: meta-llama/Llama-3.2-1B-Instruct
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label_names: ["labels"]
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# eval_on_start: True
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# eval_strategy: "steps"
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# eval_steps: 500
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# save_strategy: "no"
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# # save_steps: 500
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# logging_strategy: "steps"
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# logging_steps: 100
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# use_liger_kernel: true
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# remove_unused_columns: false
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# needed to avoid OOM by compute the metrics batch by batch
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# w/o this the trainer stores logits of all sample in memory...
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# batch_eval_metrics: true
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per_device_train_batch_size: 32
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per_device_eval_batch_size: 1
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max_val_samples_per_ds: 20
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# optim: schedule_free_adamw
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learning_rate: 0.00001
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# lr_scheduler_type: "constant_with_warmup"
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neftune_noise_alpha: 1
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weight_decay: 0.1
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warmup_ratio: 0.05
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# LoRA
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lora_r: 16
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lora_dropout: 0.05
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target_modules:
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- down_proj
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- up_proj
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- gate_proj
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# data
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train_ds_names:
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- data/raw_datasets/context_numbers_128_big
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val_ds_names:
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- data/raw_datasets/context_numbers_16
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- data/raw_datasets/context_numbers_32
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- data/raw_datasets/context_numbers_64
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- data/raw_datasets/context_numbers_128
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- data/raw_datasets/context_numbers_256
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test_ds_names:
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- data/raw_datasets/context_numbers_16
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- data/raw_datasets/context_numbers_32
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- data/raw_datasets/context_numbers_64
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- data/raw_datasets/context_numbers_128
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- data/raw_datasets/context_numbers_256
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