Tabby

This is a self-contained Tabby checkpoint: the frozen Tabby-Pretrain backbone plus a prompt. Its 99 quantile forecasts cover levels 0.01 through 0.99. The backbone weights come from pretraining step 165,000. The prompt was trained with the backbone frozen.

Resources

• Open in Colab
• TabbyTSFM GitHub code
• Technical report

Inference rule

For a requested horizon H, the model masks max(H, 128) future positions and returns the first H forecasts from that span. The backbone window is 8,192 positions. Consequently, the most observed history it can use is 8192 - max(H, 128), even when the evaluation input cap is 8,096. For example, H=96 uses at most 8,064 history points; H=168 uses at most 8,024.

The original training checkpoint records min_forecast_span=0. This release changes only the inference setting to 128. All 25 prompt tensors and all 253 backbone tensors are unchanged. config.json records min_forecast_span=128, so ordinary predict calls use the released rule without a separate flag.

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("paris-noah/Tabby", trust_remote_code=True).eval()
history = torch.randn(2, 8096).cumsum(-1)
quantiles = model.predict(history, prediction_length=96)  # [2, 99, 96]
median = quantiles[:, 49]  # 0.50 quantile

The model forecasts each variate separately. It accepts missing history values as NaN and performs its own visible-history normalization.

Architecture and training

The frozen backbone has 20 layers, width 768, 12 attention heads, and 16-step patches. The full release has 146.7 million parameters, including a 160-token prompt module. The prompt uses 16 adaptive context segments, a rank-4 context map, and a cross-attention refiner. Only the prompt was adapted after pretraining, using 48 GIFT-Eval training tasks. The released prompt has seed 42 and was selected at epoch 14; the backbone remains frozen.

GIFT-Eval results

Seasonal-Naive-normalized geometric means across the same 97 configurations, with an 8,096-point input history cap and the minimum mask span of 128:

Model	MASE	CRPS
Tabby-Pretrain, zero-shot	0.7040	0.4827
Tabby, seed 42 prompt	0.6880	0.4758

These results come from the supplied GIFT-Eval evaluation CSVs. Prompt post-training used GIFT-Eval training tasks; the corresponding leaderboard submission marks testdata_leakage as Yes. The prompt checkpoint is a new seed and the inference rule also differs from the previous seed-4 Tabby release, so their score difference does not isolate either change.

The accompanying TIME result archive contains 98 task outputs at an 8,096-point input cap for this prompted model. Its public leaderboard aggregate should be computed by TIME's official pipeline when the results are submitted.

Files and provenance

• model.safetensors: 278 tensors (253 backbone, 25 prompt), 146.7 million parameters.
• config.json: architecture, prompt settings, backbone revision, and 128-step minimum mask span.
• modeling_tabby.py, configuration_tabby.py, and their local modules: the existing Hugging Face Tabby inference implementation, which already supports this mask rule.

Code: TabbyTSFM. License: CC BY-NC 4.0.