Instructions to use mamung/2c15f6fc-8832-491b-bb73-ac4775664582 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/2c15f6fc-8832-491b-bb73-ac4775664582 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama") model = PeftModel.from_pretrained(base_model, "mamung/2c15f6fc-8832-491b-bb73-ac4775664582") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73d916c092293efdfe529ec28c8fa1679a6a624e7858b3013d56ed486483e26b
- Size of remote file:
- 202 MB
- SHA256:
- 4c53d732ce45dc94e625c0156f20885e01399a46a0db3fe72338f0cd0f1ee4e8
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