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 adapter_model.bin from cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380: direct link, hf CLI and curl.
- Browser
- Download file 332 MB
-
https://huggingface.co/cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/e4b9823d-2c04-4a0a-a420-c297cf73b380/resolve/main/adapter_model.bin
332 MB
- Xet hash:
- d9de0c7f77a8d09810d9ce6984b0abee49054ff8b02cb53b603e25458f6b3198
- Size of remote file:
- 332 MB
- SHA256:
- dfb1098fdc23e7b29be8a43e21b82d0d60d45bb9f56782e2acd8a6417a9add30
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