Instructions to use cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f: direct link, hf CLI and curl.
- Browser
- Download file 646 MB
-
https://huggingface.co/cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/resolve/main/adapter_model.bin
646 MB
- Xet hash:
- dfb5d6c54d35c4c262fcf98277f92a0bd39d8c104ea0992a4849936f781a6a8e
- Size of remote file:
- 646 MB
- SHA256:
- d47ca2006abc9db13dd5fc078060d1f2c9a15aa3de34203bffd8b17bb4abf2a3
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