Feature Extraction
Transformers
Safetensors
PyTorch
English
manas2
eeg
neuroscience
foundation-model
masked-autoencoder
representation-learning
custom_code
Instructions to use MannasAI/MANAS-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MannasAI/MANAS-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MannasAI/MANAS-2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MannasAI/MANAS-2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from MannasAI/MANAS-2: direct link, hf CLI and curl.
- Browser
- Download file 50 Bytes
-
https://huggingface.co/MannasAI/MANAS-2/resolve/main/requirements.txt
- Command line
-
hf download hf://MannasAI/MANAS-2/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/MannasAI/MANAS-2/resolve/main/requirements.txt
50 Bytes
| torch>=2.2 | |
| transformers>=4.57,<5 | |
| safetensors>=0.4 | |