Instructions to use prxy5605/b2c31d02-b7c8-485e-b52a-c9aa668d9eac with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prxy5605/b2c31d02-b7c8-485e-b52a-c9aa668d9eac with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct") model = PeftModel.from_pretrained(base_model, "prxy5605/b2c31d02-b7c8-485e-b52a-c9aa668d9eac") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcadf20db9119ecbfc5a1daaeca1a3a6d1b2b4fb8988a48537b502c4dd2151c6
- Size of remote file:
- 671 MB
- SHA256:
- 6e260cabf0d2a0e4465a88871b6c8083aab790abd182c2d359f4f018af60dbf4
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