Instructions to use nblinh/70ecb877-caac-4aeb-a35b-4406ad411d85 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/70ecb877-caac-4aeb-a35b-4406ad411d85 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "nblinh/70ecb877-caac-4aeb-a35b-4406ad411d85") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fa03e1428d9dda95ea03952ebd6385655cf85822d873e8aae9738c678df6d45
- Size of remote file:
- 228 MB
- SHA256:
- bb436da8d5ba5859ebe6df650f6dccda92a6fcb988bd193a0e28cb2b9afb3773
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