Instructions to use nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-14B-Chat") model = PeftModel.from_pretrained(base_model, "nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f: direct link, hf CLI and curl.
- Browser
- Download file 250 MB
-
https://huggingface.co/nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f/resolve/main/adapter_model.bin
- Command line
-
hf download hf://nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/nblinh63/b7bf7619-5330-4a25-a5f5-e0469bdc7c5f/resolve/main/adapter_model.bin
250 MB
- Xet hash:
- 3176e3d967f8cc34570e9283c3b00662cf7bd7d4ba4af924a11da56d4283d343
- Size of remote file:
- 250 MB
- SHA256:
- 0e29534c8338987563c4af0616b14ae659cd5c7eb51694c15750c5e66c34646b
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