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program: run_seq2seq_flax.py
entity: wandb
project: hf-flax-dalle-mini
method: random
metric:
name: eval/loss
goal: minimize
parameters:
learning_rate:
distribution: log_uniform
# from exp(min) to exp(max), ie 5e-5 to 5e-3 on log scale
min: -9.9
max: -5.3
gradient_accumulation_steps:
value: 8
warmup_steps:
# in term of optimization steps so multiplied by gradient accumulation
value: 125
command:
- python3
- ${program}
- "--train_file"
- "/data/CC12M/encoded-small-train.tsv"
- "--validation_file"
- "/data/CC12M/encoded-small-valid.tsv"
- "--output_dir"
- "./output_sweep"
- "--overwrite_output_dir"
- "--adafactor"
- "--num_train_epochs"
- 1
- "--max_train_samples"
- 1500000
- "--per_device_train_batch_size"
- 56
- "--per_device_eval_batch_size"
- 56
- "--preprocessing_num_workers"
- 80
- "--no_decay"
- "--do_train"
- "--do_eval"
- ${args}
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