Instructions to use 0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM-135M-Instruct") model = PeftModel.from_pretrained(base_model, "0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from 0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/0x1202/e41c2262-6ee1-457b-a517-b45596d9afaf/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 773d93e4d36c6c8feba47040c421ac402ccb446e00dfa7f6e76fa52b92a1a585
- Size of remote file:
- 6.84 kB
- SHA256:
- a17654caa7ad443303913d9aba9b30d18cca3f23e0376c34c990164862d98e8b
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