Instructions to use cunghoctienganh/1aaac24d-e309-48ae-b933-de333fd89fab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cunghoctienganh/1aaac24d-e309-48ae-b933-de333fd89fab 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, "cunghoctienganh/1aaac24d-e309-48ae-b933-de333fd89fab") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c05395861a83a0413479d33dddc8075ed5c43816c3007278aeebfb999f6def59
- Size of remote file:
- 84 MB
- SHA256:
- 46d57343ccc5ec2bc5db17b480d94d6022933f1d2cd2f20ae86396a9b7450ad0
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