Instructions to use dzanbek/df0fa936-3f41-4d65-8622-edc329a19b97 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/df0fa936-3f41-4d65-8622-edc329a19b97 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/llama-3-typhoon-v1.5-8b-instruct") model = PeftModel.from_pretrained(base_model, "dzanbek/df0fa936-3f41-4d65-8622-edc329a19b97") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c72aeac0a8c6df2a32c0c84089d221c59c34aad4075d7ec00a1c38f7b6bc47d
- Size of remote file:
- 6.71 kB
- SHA256:
- 1abd07d0c78e180c994aed481b8127744f759aee0dc7cff631431a6a2f78c846
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