Instructions to use minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d/resolve/main/training_args.bin
- Command line
-
hf download hf://minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/minhnguyennnnnn/ed5cee34-abdd-429b-8fb4-74ecbc317f6d/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- afc420de1adc8f5d991d2ab5079d2c5939776d666adb938eb58ce90f9c5362c3
- Size of remote file:
- 6.78 kB
- SHA256:
- 1d3766575a9b1d1afe5b5bb5feaf0eca69b4f2dc485018fd6263b1b6b7370ea4
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