Instructions to use MRNH/Feedformer-ett-hourly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRNH/Feedformer-ett-hourly with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MRNH/Feedformer-ett-hourly", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from MRNH/Feedformer-ett-hourly: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/MRNH/Feedformer-ett-hourly/resolve/main/config.json
- Command line
-
hf download hf://MRNH/Feedformer-ett-hourly/config.json
-
curl -L -o config.json https://huggingface.co/MRNH/Feedformer-ett-hourly/resolve/main/config.json
349 Bytes
| { | |
| "seq_len": 400 | |
| ,"label_len": 400 | |
| ,"pred_len": 400 | |
| ,"e_layers": 10 | |
| ,"d_layers": 13 | |
| ,"factor": 3 | |
| ,"enc_in": 1 | |
| ,"dec_in": 1 | |
| ,"c_out": 1 | |
| ,"des": "Exp" | |
| ,"d_model": 1024 | |
| ,"itr": 3 | |
| ,"dropout": 0.2 | |
| ,"activation": "relu" | |
| ,"d_ff": 4096 | |
| ,"n_heads": 128 | |
| ,"embed": "fixed" | |
| ,"freq": "h" | |
| ,"output_attention": false | |
| } |