Instructions to use minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/8e41932c-ce4f-481a-81ba-906f3d58ec3e/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- b4551ae0402d81c04228ee6ccc6a4dc803326aa6030d4504ec083be6413a686e
- Size of remote file:
- 84 MB
- SHA256:
- cd370fdfdf78284db57accc6d1a08df55ec0c04b37e5ff743e8a589fd67fb9c3
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