Instructions to use nblinh/88f6c0cf-8124-49b7-9e21-d30d565b42be with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/88f6c0cf-8124-49b7-9e21-d30d565b42be with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "nblinh/88f6c0cf-8124-49b7-9e21-d30d565b42be") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bc83fa54774e54e03616a76e23f8aac6902852a680f9557bcf6f7d0367dd0d9
- Size of remote file:
- 6.78 kB
- SHA256:
- eff5aad5b4ed9c3dc82e53acd1efb56897eefed1b20a5812f44b5da86160f8d3
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