Instructions to use minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60/resolve/main/training_args.bin
- Command line
-
hf download hf://minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- e291b9ff91c51b7dd3edead646bd6ac849b557c8705f4e45680e37b307837bce
- Size of remote file:
- 6.78 kB
- SHA256:
- 5d1ef47e5ac79efc1399cba0c40681d5954b5febfecdb7410886dc316a6581e2
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