Instructions to use cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Phi-3-mini-4k-instruct") model = PeftModel.from_pretrained(base_model, "cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/d64fa5ce-cbf1-455e-ae25-e2961c53f7d7/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 94be9f85fef834e8a3bfa778b8fb0b05ef8a817b24f4734e2651edfe4373a14c
- Size of remote file:
- 6.84 kB
- SHA256:
- 2ca48087abb2320a3f0be10922334e44597f43ba67f8914e55bd2186ac710f2e
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