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/optimizer.pt from vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb: direct link, hf CLI and curl.
- Browser
- Download file 145 MB
-
https://huggingface.co/vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/vmpsergio/f35ca38f-8c45-416b-8ad7-f85b71d45ffb/resolve/main/last-checkpoint/optimizer.pt
145 MB
- Xet hash:
- 3ffad4facaa3b532778ea00a31679cc1557fae92e9619acc7b1e906df8da3458
- Size of remote file:
- 145 MB
- SHA256:
- 887ff07c00d031a39b0ba1dac930e46878bfe35ea70bcd973da896b8116c8886
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