Transformers
English
transformer
language-models
long-context
memory-augmented-transformers
eidosformer
causal-language-modeling
ai-research
neural-architecture
episodic-memory
semantic-memory
kNN-inference
compressive-transformer
llama-family
PyTorch
Instructions to use Himan-de/EidosFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Himan-de/EidosFormer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Himan-de/EidosFormer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 396e0fc970a24ac1c7f2761b5590aaa9fd3043206d738a672ca0fa6027c559b6
- Size of remote file:
- 5.08 GB
- SHA256:
- 58d814a39729d7c39ccfebfd16b7c034eeffa05fa56009ff0fa9045de4740c13
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