Instructions to use nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Instruct-hf") model = PeftModel.from_pretrained(base_model, "nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099/resolve/main/training_args.bin
- Command line
-
hf download hf://nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nhungphammmmm/2b68371d-df86-4324-ade6-12c43bb36099/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 4856778f905237ac991d0dac25c978ec43b20aacf6875a654ad3524441e38ac5
- Size of remote file:
- 6.78 kB
- SHA256:
- 7274a81cd72ad09f7ed41133d969ec4518d30a0ba4a75cec0467cbb55dd5fffd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.