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/scheduler.pt from infogep/ab57643b-7870-4273-a353-e0311e8b4b87: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/infogep/ab57643b-7870-4273-a353-e0311e8b4b87/resolve/main/last-checkpoint/scheduler.pt
- Command line
-
hf download hf://infogep/ab57643b-7870-4273-a353-e0311e8b4b87/last-checkpoint/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/infogep/ab57643b-7870-4273-a353-e0311e8b4b87/resolve/main/last-checkpoint/scheduler.pt
1.06 kB
- Xet hash:
- c335935bbc03929db76105ff74224810454767d3f1c2fee930e2b1dab8ec4c1b
- Size of remote file:
- 1.06 kB
- SHA256:
- 9c971b41fe7521762372f4d846fdb25f7ae6aeaf0776fa4aa30da6edd7f567bf
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