Instructions to use dada22231/b4ef3876-be34-4542-8f13-5dd46d981a67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/b4ef3876-be34-4542-8f13-5dd46d981a67 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("jhflow/mistral7b-lora-multi-turn-v2") model = PeftModel.from_pretrained(base_model, "dada22231/b4ef3876-be34-4542-8f13-5dd46d981a67") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc406ebeab8c3c2914d40d8785667d1628af24443018f531682cb32527142b43
- Size of remote file:
- 6.84 kB
- SHA256:
- a5664f352b35b9d23a2e985d5d1857e2d38b679358df966e890e13b83aa60adb
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