Instructions to use alexgrigoras/sdg_chronos_t5_small_dunnhumby with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexgrigoras/sdg_chronos_t5_small_dunnhumby with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alexgrigoras/sdg_chronos_t5_small_dunnhumby", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,156 Bytes
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"model_name": "amazon/chronos-t5-small",
"base_model_id": "amazon/chronos-t5-small",
"context_length": 140,
"prediction_length": 30,
"num_bins": 4094,
"value_range": [
-5.0,
5.0
],
"learning_rate": 2.5e-05,
"train_steps": 300,
"lora_rank": 32,
"lora_alpha": 64,
"batch_size": 2,
"gradient_accumulation_steps": 8,
"max_source_length": 768,
"max_target_length": 256,
"random_state": 42,
"task_prefix": "generate synthetic retail demand future from historical context",
"seasonality_strength": 0.6,
"seasonal_period": 7,
"seasonal_fallback_strength": 0.25,
"zero_threshold_for_sparsity": 0.6,
"prefer_backend": "lora",
"use_special_tokens": true,
"add_calendar_features": true,
"warmup_ratio": 0.05,
"weight_decay": 0.01,
"privacy_reference_max_windows": 2000,
"privacy_min_distance_quantile": 0.25,
"privacy_distance_penalty": 4.0,
"privacy_noise_strength": 0.1,
"privacy_baseline_blend": 0.25,
"privacy_training_jitter_prob": 0.6,
"privacy_training_jitter_strength": 0.1,
"privacy_deduplicate_examples": true,
"privacy_filter_enabled": true,
"privacy_filter_max_retries": 6
} |