Instructions to use cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("zake7749/gemma-2-2b-it-chinese-kyara-dpo") model = PeftModel.from_pretrained(base_model, "cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 21b0553c2783fbd0c96b57ed5a9a6e2276d37a4ab947cd9a49bfb7e1709fa6db
- Size of remote file:
- 6.84 kB
- SHA256:
- 56c039105f5577abc1eeb6984cbcd1bcd14b21f2277c34a0b86305d9f1cb55b0
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