Instructions to use mrhunghd/f1631a0d-ccd2-49c1-8dce-a7d76efe8270 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mrhunghd/f1631a0d-ccd2-49c1-8dce-a7d76efe8270 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-7B-Instruct") model = PeftModel.from_pretrained(base_model, "mrhunghd/f1631a0d-ccd2-49c1-8dce-a7d76efe8270") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f220a22dfb63020ff2eaac1bb86c487abc7a1d1009dd5956c987a83334aa7be0
- Size of remote file:
- 6.78 kB
- SHA256:
- 33400817741bcc2abda9d3c1d7162999e4e8abfbe540c446cb0300933ef09847
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.