Instructions to use minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f 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, "minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/e33a8a38-dc7f-4987-be49-bb848bed027f/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- d478635a5a1dbed1112a8d1a2787febc4421aa76a623e80e2dfb073c0b7111bb
- Size of remote file:
- 84 MB
- SHA256:
- b4b1449466b36a3c6af4d0e00c7ef06160e1b8fdd726858f2c2f2318bc952911
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