Instructions to use dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8: direct link, hf CLI and curl.
- Browser
- Download file 68.9 kB
-
https://huggingface.co/dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dimasik87/4ac67ab7-aaa5-46f9-b3dd-d935c7b307b8/resolve/main/last-checkpoint/optimizer.pt
68.9 kB
- Xet hash:
- 63c9d9d8a95648be0038c623cbc94f293599316e484a198c74a51cc99ddc043d
- Size of remote file:
- 68.9 kB
- SHA256:
- 83dc748da72ddadf24f4728d7fd837b1d8413f6ed8ef63ece337fdbf34753f67
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