Instructions to use fats-fme/befa1a68-b759-41cd-aa37-79f4aaa9a6a5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/befa1a68-b759-41cd-aa37-79f4aaa9a6a5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "fats-fme/befa1a68-b759-41cd-aa37-79f4aaa9a6a5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30c5b853806d1853c6d4f4481024936be3133c04b6abdf377738c55710470ee4
- Size of remote file:
- 6.78 kB
- SHA256:
- 93228c1ecbadbff2eb20e491a1418223311219e72b84141c2b992a90489538d8
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