Instructions to use maydixit/llama_v2_tma_3e_masked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maydixit/llama_v2_tma_3e_masked with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("maydixit/llama_v2_tma_3e_masked", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e36580a2c491a978d5288a6359df9497de01ebb0a2ff1c306a91cb41d8e8361a
- Size of remote file:
- 545 MB
- SHA256:
- c7198a41c312f5aa4622c6ab90718d7d854dbd7537835e2642418bfbf9d7330e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.