| --- |
| library_name: transformers |
| pipeline_tag: time-series-forecasting |
| tags: |
| - time-series |
| - synthetic-data |
| - seq2seq |
| - retail |
| - qlora |
| base_model: amazon/chronos-t5-small |
| --- |
| |
| # alexgrigoras/sdg_chronos_t5_small_dunnhumby |
|
|
| Synthetic time-series generation checkpoint for the DIF-PI framework. |
|
|
| ## Model summary |
|
|
| This checkpoint is trained as a seq2seq generator on tokenized retail demand windows. It uses a T5-style encoder-decoder backbone, QLoRA when available, extended time-series special tokens, calendar conditioning, multiple-sample generation, and a seasonality-aware calibration step at inference time. |
|
|
| ## Intended use |
|
|
| The model is intended for research on synthetic retail demand generation and validation inside the DIF-PI framework. It is not intended for safety-critical or fully autonomous business decisions without human review. |
|
|
| ## Training setup |
|
|
| - Base model: amazon/chronos-t5-small |
| - Context length: 140 |
| - Prediction length: 30 |
| - Quantization bins: 4094 |
| - Backend: lora |
|
|
