A better sampler like nutpie (even better with low-rank adaptation) may help. Your data is small, that I would invite you to try the numba backend (default if you go with nutpie). It’s usually the best performing on CPU, although not always. Also suggest you update to newer pymc > 6.x, we did a lot of improvements on the numba backend: PyMC 6.0 & PyTensor 3.0: ecosystem updates — PyMC project website
If you don’t see any improvements, we may need to look at the model like bob hinted at
| # | Наименование новости | Тональность | Информативность | Дата публикации |
|---|---|---|---|---|
| 1 | 🚀 Release pymc-extras v0.13.0 | 0 | 19.63 | 13-07-2026 |
| 2 | 🚀 Release pymc-extras v0.14.0 | 0 | 19.63 | 28-07-2026 |
| 3 | 🚀 Release pymc-extras v0.13.1 | 0 | 19.63 | 19-07-2026 |
| 4 | Arviz Issues following Tutorials | 0 | 7.44 | 21-07-2026 |
| 5 | New contributor looking to get started — background in ML/RAG research | 0 | 10.42 | 23-07-2026 |
| 6 | Hidden Markov Models in PyMC: marginalize and recover a DiscreteMarkovChain | 0 | 4.78 | 13-07-2026 |
| 7 | Pymc_forecast — a new Bayesian time-series forecasting toolkit for PyMC | 0 | 19.35 | 17-07-2026 |
| 8 | 🚀 Release v6.2.0 | 0 | 18.52 | 23-07-2026 |
| 9 | Media Mix Modelling | 0 | 11.74 | 19-07-2026 |