Instructions to use minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/7f01aeb2-b835-4daa-97b3-181780718c6f/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- b499325037d1a792110b1b0e53bcb6adad440c2fc046cbed38043e3e2e60d2fb
- Size of remote file:
- 84 MB
- SHA256:
- 84d00141c48752e86355f3b36b68248c83a03a16cc838a8e5d9ec690690fae3d
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