Instructions to use tarabukinivan/d7a145cd-4d6e-4767-acb4-47ece7e17cdc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tarabukinivan/d7a145cd-4d6e-4767-acb4-47ece7e17cdc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-1.7B") model = PeftModel.from_pretrained(base_model, "tarabukinivan/d7a145cd-4d6e-4767-acb4-47ece7e17cdc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ea3795ab5d953c9186fea3a87763fa80d8c1c5a5f104627a574a4da1d77e740
- Size of remote file:
- 145 MB
- SHA256:
- 5726d63e4ce8a403eccd56097390137f3ecc226e70bc67d2c7aabc90547b3743
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