Instructions to use mamung/a68fc4d2-004a-4b95-8abd-402434667dd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/a68fc4d2-004a-4b95-8abd-402434667dd3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-2-7b-chat") model = PeftModel.from_pretrained(base_model, "mamung/a68fc4d2-004a-4b95-8abd-402434667dd3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcd008c9ccdaaf946f878e45f21a4e6fcc823947c75b3ca3d10d5970ea27a618
- Size of remote file:
- 6.84 kB
- SHA256:
- ca413de0424d16dae62564117f8b59621b921b283014cbd5c125b9ca80212ae0
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