Instructions to use cimol/50a30448-b07e-488d-b687-462b4f7ad10f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/50a30448-b07e-488d-b687-462b4f7ad10f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-2-7b-chat") model = PeftModel.from_pretrained(base_model, "cimol/50a30448-b07e-488d-b687-462b4f7ad10f") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/50a30448-b07e-488d-b687-462b4f7ad10f: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/50a30448-b07e-488d-b687-462b4f7ad10f/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/50a30448-b07e-488d-b687-462b4f7ad10f/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/50a30448-b07e-488d-b687-462b4f7ad10f/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- b2e8561dbc19a43487c16001bb8dadc8d9c754cf557fc7248968219a6762b051
- Size of remote file:
- 6.84 kB
- SHA256:
- 2d382aabd8c9273379d56cf1eb7bc833dfabdf32e3ef7e71de70461c8e54f584
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