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:
- 89ed935902355f18dddeb19d3eb9ace09b2c591298a795a32ca69412598cdfa7
- Size of remote file:
- 640 MB
- SHA256:
- 821bc7abaf37a55483bfba6d4f3ee0f33ad960c1fc55ddd5b63b5a1a5c52064b
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