Transformers
English
mla-attention
multi-head-latent-attention
flow-matching
rectified-flow
on-device
efficient-attention
smol-scale
research
proof-of-concept
Instructions to use Tinman-Lab/Tinman-SmolOmni-MLA-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tinman-Lab/Tinman-SmolOmni-MLA-500M with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tinman-Lab/Tinman-SmolOmni-MLA-500M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K