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 training_args.bin from infogep/ab57643b-7870-4273-a353-e0311e8b4b87: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/infogep/ab57643b-7870-4273-a353-e0311e8b4b87/resolve/main/training_args.bin
- Command line
-
hf download hf://infogep/ab57643b-7870-4273-a353-e0311e8b4b87/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/infogep/ab57643b-7870-4273-a353-e0311e8b4b87/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 50ac341ca1f64e01f8d501cb60ca1e58becdaaa49b4f3c812679a1aca0c6b0c9
- Size of remote file:
- 6.78 kB
- SHA256:
- a15fb0c18f26ac959d24a954787d2378a559c388ab4abca8dc6728a0e65099fb
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