Instructions to use nblinh63/8bd5f521-94be-47bb-826d-16798069fb0d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/8bd5f521-94be-47bb-826d-16798069fb0d 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, "nblinh63/8bd5f521-94be-47bb-826d-16798069fb0d") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c369c6f96f9255490b84bf77a01588b050c19588b409a39a446119ca576dc42b
- Size of remote file:
- 6.78 kB
- SHA256:
- 7eed74f4309734682acd8da234a0f56761216414ab25c69af964897a48d9d0d3
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