Instructions to use adammandic87/a9cbb793-6a69-477f-9d7a-38d5701a3035 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/a9cbb793-6a69-477f-9d7a-38d5701a3035 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/llama-3-typhoon-v1.5-8b-instruct") model = PeftModel.from_pretrained(base_model, "adammandic87/a9cbb793-6a69-477f-9d7a-38d5701a3035") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e1a03cd420e19c7dd3efc3577a6e3c39616a271a70308c0d2fcbe7728d6d43f
- Size of remote file:
- 6.78 kB
- SHA256:
- 1392492cfcdec8942a3c726246fca26e0365f826293bc5e84e2f338e1dc69d29
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