Instructions to use cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("samoline/4f3782a6-7b9f-4ebe-b488-6b278dfed6e4") model = PeftModel.from_pretrained(base_model, "cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c/resolve/main/training_args.bin
- Command line
-
hf download hf://cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cvoffer/ca49da4b-0b0c-4714-9568-1ff2eaac2b6c/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 8cb847b7d556d26750cbda7ded1cfbba79af3bf51bf5ba9216a057e3f6749a2e
- Size of remote file:
- 6.78 kB
- SHA256:
- 8a1a48c1c92107de1b48f16c215f997775472b8f2bbc2b8f7b2dccf73130b4ee
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