Instructions to use nblinh/e2a56e86-7868-4b78-9af5-9e021eb796a7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/e2a56e86-7868-4b78-9af5-9e021eb796a7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.2") model = PeftModel.from_pretrained(base_model, "nblinh/e2a56e86-7868-4b78-9af5-9e021eb796a7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 384dcbcf1a6e4cfd93cbfc6d6aecb7f33f5b2b3e840a01276fa079c8d8d2af02
- Size of remote file:
- 83.9 MB
- SHA256:
- 085c6f7d111c4e6bc38012beea6d3720e6d4e7e22c35e40b0805b16e8fbe5cc1
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