Instructions to use vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-1.7B") model = PeftModel.from_pretrained(base_model, "vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 263e2d030214b49307fad5cb6b0a3a7890fbc7d24777d313c4978114600aebf4
- Size of remote file:
- 6.78 kB
- SHA256:
- e4e1593612432e9a7e7642bed7c091a14f30bc9d0317de37ac5ae8c01267473a
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