Instructions to use infogep/ab57643b-7870-4273-a353-e0311e8b4b87 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use infogep/ab57643b-7870-4273-a353-e0311e8b4b87 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, "infogep/ab57643b-7870-4273-a353-e0311e8b4b87") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from infogep/ab57643b-7870-4273-a353-e0311e8b4b87: direct link, hf CLI and curl.
- Browser
- Download file 5.96 MB
-
https://huggingface.co/infogep/ab57643b-7870-4273-a353-e0311e8b4b87/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://infogep/ab57643b-7870-4273-a353-e0311e8b4b87/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/infogep/ab57643b-7870-4273-a353-e0311e8b4b87/resolve/main/last-checkpoint/optimizer.pt
5.96 MB
- Xet hash:
- 21bd62d79ea40a8c063f3c1a217cd77cd0a89f1b6b116628f9f432397a67b2e0
- Size of remote file:
- 5.96 MB
- SHA256:
- 177698af163c97b34abc2346e6f29d1a23dca65ff7d0a39285914f3e11d8f49d
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