Instructions to use emilyseong/pmc_attnpool_gpu4_cumsum_spi2e-5_proj2e-5_llm2e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use emilyseong/pmc_attnpool_gpu4_cumsum_spi2e-5_proj2e-5_llm2e-5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/llava-med-v1.5-mistral-7b") model = PeftModel.from_pretrained(base_model, "emilyseong/pmc_attnpool_gpu4_cumsum_spi2e-5_proj2e-5_llm2e-5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c020910e0650cec40ca0d8d4df0728d243b38df723e9eadaf1d21fdeb08880ba
- Size of remote file:
- 781 MB
- SHA256:
- bc89a06926c8109e0aa2e0560a26535f63554b37b565bfb7b6e615ffa0f328ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.