Instructions to use minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60: direct link, hf CLI and curl.
- Browser
- Download file 37.1 MB
-
https://huggingface.co/minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/10d53461-c17f-4b8b-b99d-d98a72e7ed60/resolve/main/adapter_model.bin
37.1 MB
- Xet hash:
- a61ff9e0a008c382a93da6bf4aa3ec91b700e96c333638f717f994c56b1bfca0
- Size of remote file:
- 37.1 MB
- SHA256:
- cdbd57e61e356ac3be68db68cce7011bd5f643f64a3f63aa167a9a5050c9148a
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