Instructions to use nblinh63/e0779520-1b67-4e5a-b438-9775d5fdd111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/e0779520-1b67-4e5a-b438-9775d5fdd111 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "nblinh63/e0779520-1b67-4e5a-b438-9775d5fdd111") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e865b23b137f4b992de56ce94eb2bb86526952836b7fe70898d15a80642a25e1
- Size of remote file:
- 168 MB
- SHA256:
- 6b3b3a9199b41c71b96aed9e1f1943731788b5b60acd52cd5354a669702e1946
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