mirror of
https://github.com/amazon-science/chronos-forecasting
synced 2026-05-24 01:58:27 +00:00
2f92a126d3
*Description of changes:* This PR adds support for training causal/decoder-only models. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
35 lines
755 B
YAML
35 lines
755 B
YAML
training_data_paths:
|
|
- "/home/ubuntu/tsmixup-data.arrow"
|
|
- "/home/ubuntu/kernelsynth-data.arrow"
|
|
probability:
|
|
- 0.9
|
|
- 0.1
|
|
context_length: 512
|
|
prediction_length: 64
|
|
min_past: 60
|
|
max_steps: 200_000
|
|
save_steps: 100_000
|
|
log_steps: 500
|
|
per_device_train_batch_size: 32
|
|
learning_rate: 0.001
|
|
optim: adamw_torch_fused
|
|
num_samples: 20
|
|
shuffle_buffer_length: 100_000
|
|
gradient_accumulation_steps: 1
|
|
model_id: openai-community/gpt2
|
|
model_type: causal
|
|
random_init: false
|
|
tie_embeddings: false
|
|
output_dir: ./output/
|
|
tf32: true
|
|
torch_compile: true
|
|
tokenizer_class: "MeanScaleUniformBins"
|
|
tokenizer_kwargs:
|
|
low_limit: -15.0
|
|
high_limit: 15.0
|
|
n_tokens: 4096
|
|
lr_scheduler_type: linear
|
|
warmup_ratio: 0.0
|
|
dataloader_num_workers: 1
|
|
max_missing_prop: 0.1
|
|
use_eos_token: true
|