Time Series Forecasting
Transformers
Safetensors
fela-pdm
feature-extraction
fela
fourier-neural-operator
fno
cpu
on-device
predictive-maintenance
time-series
anomaly-detection
custom_code
Instructions to use lowdown-labs/fela-pdm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowdown-labs/fela-pdm with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lowdown-labs/fela-pdm", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Quickstart | |
| Load a FELA-PdM head and run one real sensor window on CPU. Uses the self contained loader | |
| in `../modeling.py`. | |
| ## Steps | |
| 1. Install the pinned requirements (CPU PyTorch): | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 2. Get the weights. One safetensors file per head ships in this repo | |
| (`cmapss_FD001.safetensors`, `cwru.safetensors`) beside `config.json`. Point at the repo | |
| directory and pass `--variant` to pick the head: | |
| ```bash | |
| export FELA_PDM_WEIGHTS=/path/to/weights_dir # holds <variant>.safetensors + config.json | |
| ``` | |
| 3. Run: | |
| ```bash | |
| python run.py --variant cmapss_FD001 # remaining useful life | |
| python run.py --variant cwru # bearing fault class | |
| ``` | |
| ## Few line load from Python | |
| ```python | |
| from modeling import load_model | |
| m = load_model("/path/to/weights_dir", variant="cmapss_FD001") # dir, .pt, or HF repo id | |
| rul = m.predict(window) # window: (1, 30, 14) sensor cycles | |
| ``` | |
| The C-MAPSS RUL head expects 30 cycles of 14 sensors (min max normalized on the training | |
| statistics). The CWRU head expects 2048 raw vibration samples (12 kHz, per signal | |
| standardized). See `modeling.preprocess_cmapss` and `modeling.preprocess_cwru` for the exact | |
| preprocessing, and `modeling.validate_window` for input validation. | |