Instructions to use lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("JackFram/llama-160m") model = PeftModel.from_pretrained(base_model, "lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27/resolve/main/training_args.bin
- Command line
-
hf download hf://lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 2d71519f440062dc77855aa0df8ea3a8ef7d79c608b86d3eca20bd7cc22a668f
- Size of remote file:
- 6.78 kB
- SHA256:
- 3e1e88cb8767a25db7f31f7c776c3cfd62998e4f87a62956263071c26558a3ad
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