README.md

metadata

title: TimeBraid
emoji: 🧵
colorFrom: indigo
colorTo: gray
sdk: gradio
sdk_version: 5.50.0
app_file: app.py
short_description: Understand and forecast time series with language
python_version: '3.12'
startup_duration_timeout: 1h
pinned: false

TimeBraid

Interactive demo for TimeBraid-2.5B — Unifying Time Series and Language for Understanding and Forecasting.

• Paper: https://huggingface.co/papers/2609.29792
• Model: XinyueWangg/TimeBraid-2.5B
• Code: https://github.com/CharonWangg/TimeBraid

TimeBraid braids a Qwen3-1.7B language backbone with a TimesFM 2.5 time-series expert through an interleaved Mixture-of-Transformers, so numeric series and text live in the same token stream. The same numbers can therefore be read very differently depending on the context you give them.

How to use it

1. Paste one series per line — comma-separated numbers. One line = one series; two lines = two related series the model can compare or choose between.
2. Write your question or instruction. Ask for an explanation of the observed data, or ask for future values.
3. Set the forecast horizon. 0 keeps the run in understanding mode and the model answers in words. A positive N switches to forecasting and the model returns N future values for one target series, plotted against the history.
4. Textual context matters for forecasts: describing an upcoming promotion, a heatwave, or a school term materially changes the predicted numbers.

Advanced options hold an optional system prompt, a 1-based selector for which input series to forecast, and the text-generation budget.

Implementation notes

• Runs on ZeroGPU with the full BF16 checkpoint resident on the GPU.
• The checkpoint vendors its own timebraid inference package, loaded through trust_remote_code=True; no separate TimesFM checkpoint is required.
• MoT mixed attention in TimeBraid is FlashAttention-2 only, and FA3/FA4 have no sm_120 kernels, so the Space installs the prebuilt Blackwell FA2 wheel.
• Decoding follows the released recipe: greedy (do_sample=False), one returned sequence, one request at a time.

Attribution

The example requests in the UI are the illustrative tasks from the TimeBraid model card and the authors' examples/inference_tasks.ipynb (Apache-2.0).