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:
- 0ed7b3ae5bb86027535329e00c25c1c7d810e406cd8cde3c7c2434d1cbb77e85
- Size of remote file:
- 14.2 kB
- SHA256:
- 4c8760177d65af4dda280e8136e6bc81afef274307ad17a1a5d29adc7f7ea52a
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